Overview

Dataset statistics

Number of variables89
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.2 KiB
Average record size in memory714.1 B

Variable types

Numeric24
Categorical65

Warnings

Stabr has constant value "NY" Constant
area_name has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2000 has a high cardinality: 62 distinct values High cardinality
Employed_2000 has a high cardinality: 62 distinct values High cardinality
Unemployed_2000 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2001 has a high cardinality: 62 distinct values High cardinality
Employed_2001 has a high cardinality: 62 distinct values High cardinality
Unemployed_2001 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2002 has a high cardinality: 62 distinct values High cardinality
Employed_2002 has a high cardinality: 62 distinct values High cardinality
Unemployed_2002 has a high cardinality: 60 distinct values High cardinality
Civilian_labor_force_2003 has a high cardinality: 62 distinct values High cardinality
Employed_2003 has a high cardinality: 62 distinct values High cardinality
Unemployed_2003 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2004 has a high cardinality: 62 distinct values High cardinality
Employed_2004 has a high cardinality: 62 distinct values High cardinality
Unemployed_2004 has a high cardinality: 61 distinct values High cardinality
Civilian_labor_force_2005 has a high cardinality: 62 distinct values High cardinality
Employed_2005 has a high cardinality: 62 distinct values High cardinality
Unemployed_2005 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2006 has a high cardinality: 62 distinct values High cardinality
Employed_2006 has a high cardinality: 62 distinct values High cardinality
Unemployed_2006 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2007 has a high cardinality: 62 distinct values High cardinality
Employed_2007 has a high cardinality: 62 distinct values High cardinality
Unemployed_2007 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2008 has a high cardinality: 62 distinct values High cardinality
Employed_2008 has a high cardinality: 62 distinct values High cardinality
Unemployed_2008 has a high cardinality: 61 distinct values High cardinality
Civilian_labor_force_2009 has a high cardinality: 62 distinct values High cardinality
Employed_2009 has a high cardinality: 62 distinct values High cardinality
Unemployed_2009 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2010 has a high cardinality: 62 distinct values High cardinality
Employed_2010 has a high cardinality: 62 distinct values High cardinality
Unemployed_2010 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2011 has a high cardinality: 62 distinct values High cardinality
Employed_2011 has a high cardinality: 62 distinct values High cardinality
Unemployed_2011 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2012 has a high cardinality: 62 distinct values High cardinality
Employed_2012 has a high cardinality: 62 distinct values High cardinality
Unemployed_2012 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2013 has a high cardinality: 62 distinct values High cardinality
Employed_2013 has a high cardinality: 62 distinct values High cardinality
Unemployed_2013 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2014 has a high cardinality: 62 distinct values High cardinality
Employed_2014 has a high cardinality: 62 distinct values High cardinality
Unemployed_2014 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2015 has a high cardinality: 62 distinct values High cardinality
Employed_2015 has a high cardinality: 62 distinct values High cardinality
Unemployed_2015 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2016 has a high cardinality: 62 distinct values High cardinality
Employed_2016 has a high cardinality: 61 distinct values High cardinality
Unemployed_2016 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2017 has a high cardinality: 62 distinct values High cardinality
Employed_2017 has a high cardinality: 62 distinct values High cardinality
Unemployed_2017 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2018 has a high cardinality: 62 distinct values High cardinality
Employed_2018 has a high cardinality: 62 distinct values High cardinality
Unemployed_2018 has a high cardinality: 62 distinct values High cardinality
Civilian_labor_force_2019 has a high cardinality: 62 distinct values High cardinality
Employed_2019 has a high cardinality: 62 distinct values High cardinality
Unemployed_2019 has a high cardinality: 61 distinct values High cardinality
Median_Household_Income_2018 has a high cardinality: 62 distinct values High cardinality
df_index is highly correlated with FIPStxtHigh correlation
FIPStxt is highly correlated with df_indexHigh correlation
Rural_urban_continuum_code_2013 is highly correlated with Urban_influence_code_2013High correlation
Urban_influence_code_2013 is highly correlated with Rural_urban_continuum_code_2013High correlation
Unemployment_rate_2000 is highly correlated with Unemployment_rate_2001High correlation
Unemployment_rate_2001 is highly correlated with Unemployment_rate_2000 and 4 other fieldsHigh correlation
Unemployment_rate_2002 is highly correlated with Unemployment_rate_2001 and 2 other fieldsHigh correlation
Unemployment_rate_2003 is highly correlated with Unemployment_rate_2001 and 2 other fieldsHigh correlation
Unemployment_rate_2004 is highly correlated with Unemployment_rate_2001 and 3 other fieldsHigh correlation
Unemployment_rate_2005 is highly correlated with Unemployment_rate_2001 and 5 other fieldsHigh correlation
Unemployment_rate_2006 is highly correlated with Unemployment_rate_2005 and 6 other fieldsHigh correlation
Unemployment_rate_2007 is highly correlated with Unemployment_rate_2005 and 3 other fieldsHigh correlation
Unemployment_rate_2008 is highly correlated with Unemployment_rate_2006 and 1 other fieldsHigh correlation
Unemployment_rate_2009 is highly correlated with Unemployment_rate_2006 and 2 other fieldsHigh correlation
Unemployment_rate_2010 is highly correlated with Unemployment_rate_2006 and 5 other fieldsHigh correlation
Unemployment_rate_2011 is highly correlated with Unemployment_rate_2006 and 4 other fieldsHigh correlation
Unemployment_rate_2012 is highly correlated with Unemployment_rate_2010 and 3 other fieldsHigh correlation
Unemployment_rate_2013 is highly correlated with Unemployment_rate_2005 and 4 other fieldsHigh correlation
Unemployment_rate_2014 is highly correlated with Unemployment_rate_2005 and 5 other fieldsHigh correlation
Unemployment_rate_2015 is highly correlated with Unemployment_rate_2016High correlation
Unemployment_rate_2016 is highly correlated with Unemployment_rate_2015 and 3 other fieldsHigh correlation
Unemployment_rate_2017 is highly correlated with Unemployment_rate_2016 and 2 other fieldsHigh correlation
Unemployment_rate_2018 is highly correlated with Unemployment_rate_2016 and 2 other fieldsHigh correlation
Unemployment_rate_2019 is highly correlated with Unemployment_rate_2016 and 2 other fieldsHigh correlation
Employed_2001 is highly correlated with Civilian_labor_force_2007 and 63 other fieldsHigh correlation
Civilian_labor_force_2007 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2014 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2009 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2004 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2005 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2002 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2015 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2007 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2012 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2014 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2011 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2014 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2009 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2006 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2013 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2011 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2001 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2003 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2008 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2012 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2018 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2000 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2010 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2019 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2005 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2000 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2007 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2017 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2005 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2013 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2002 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Civilian_labor_force_2016 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2018 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2003 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2008 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2009 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2010 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2006 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2012 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2016 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Employed_2015 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2017 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployment_rate_2015 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Employed_2018 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2019 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2010 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2015 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2017 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2016 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2003 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2013 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Median_Household_Income_2018 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Civilian_labor_force_2004 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Metro_2013 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Civilian_labor_force_2011 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2004 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
area_name is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2008 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Unemployed_2019 is highly correlated with Employed_2001 and 57 other fieldsHigh correlation
Civilian_labor_force_2001 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2000 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Stabr is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Employed_2002 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
Unemployed_2006 is highly correlated with Employed_2001 and 63 other fieldsHigh correlation
df_index is uniformly distributed Uniform
FIPStxt is uniformly distributed Uniform
area_name is uniformly distributed Uniform
Civilian_labor_force_2000 is uniformly distributed Uniform
Employed_2000 is uniformly distributed Uniform
Unemployed_2000 is uniformly distributed Uniform
Civilian_labor_force_2001 is uniformly distributed Uniform
Employed_2001 is uniformly distributed Uniform
Unemployed_2001 is uniformly distributed Uniform
Civilian_labor_force_2002 is uniformly distributed Uniform
Employed_2002 is uniformly distributed Uniform
Unemployed_2002 is uniformly distributed Uniform
Civilian_labor_force_2003 is uniformly distributed Uniform
Employed_2003 is uniformly distributed Uniform
Unemployed_2003 is uniformly distributed Uniform
Civilian_labor_force_2004 is uniformly distributed Uniform
Employed_2004 is uniformly distributed Uniform
Unemployed_2004 is uniformly distributed Uniform
Civilian_labor_force_2005 is uniformly distributed Uniform
Employed_2005 is uniformly distributed Uniform
Unemployed_2005 is uniformly distributed Uniform
Civilian_labor_force_2006 is uniformly distributed Uniform
Employed_2006 is uniformly distributed Uniform
Unemployed_2006 is uniformly distributed Uniform
Civilian_labor_force_2007 is uniformly distributed Uniform
Employed_2007 is uniformly distributed Uniform
Unemployed_2007 is uniformly distributed Uniform
Civilian_labor_force_2008 is uniformly distributed Uniform
Employed_2008 is uniformly distributed Uniform
Unemployed_2008 is uniformly distributed Uniform
Civilian_labor_force_2009 is uniformly distributed Uniform
Employed_2009 is uniformly distributed Uniform
Unemployed_2009 is uniformly distributed Uniform
Civilian_labor_force_2010 is uniformly distributed Uniform
Employed_2010 is uniformly distributed Uniform
Unemployed_2010 is uniformly distributed Uniform
Civilian_labor_force_2011 is uniformly distributed Uniform
Employed_2011 is uniformly distributed Uniform
Unemployed_2011 is uniformly distributed Uniform
Civilian_labor_force_2012 is uniformly distributed Uniform
Employed_2012 is uniformly distributed Uniform
Unemployed_2012 is uniformly distributed Uniform
Civilian_labor_force_2013 is uniformly distributed Uniform
Employed_2013 is uniformly distributed Uniform
Unemployed_2013 is uniformly distributed Uniform
Civilian_labor_force_2014 is uniformly distributed Uniform
Employed_2014 is uniformly distributed Uniform
Unemployed_2014 is uniformly distributed Uniform
Civilian_labor_force_2015 is uniformly distributed Uniform
Employed_2015 is uniformly distributed Uniform
Unemployed_2015 is uniformly distributed Uniform
Civilian_labor_force_2016 is uniformly distributed Uniform
Employed_2016 is uniformly distributed Uniform
Unemployed_2016 is uniformly distributed Uniform
Civilian_labor_force_2017 is uniformly distributed Uniform
Employed_2017 is uniformly distributed Uniform
Unemployed_2017 is uniformly distributed Uniform
Civilian_labor_force_2018 is uniformly distributed Uniform
Employed_2018 is uniformly distributed Uniform
Unemployed_2018 is uniformly distributed Uniform
Civilian_labor_force_2019 is uniformly distributed Uniform
Employed_2019 is uniformly distributed Uniform
Unemployed_2019 is uniformly distributed Uniform
Median_Household_Income_2018 is uniformly distributed Uniform
df_index has unique values Unique
FIPStxt has unique values Unique
area_name has unique values Unique
Civilian_labor_force_2000 has unique values Unique
Employed_2000 has unique values Unique
Unemployed_2000 has unique values Unique
Civilian_labor_force_2001 has unique values Unique
Employed_2001 has unique values Unique
Unemployed_2001 has unique values Unique
Civilian_labor_force_2002 has unique values Unique
Employed_2002 has unique values Unique
Civilian_labor_force_2003 has unique values Unique
Employed_2003 has unique values Unique
Unemployed_2003 has unique values Unique
Civilian_labor_force_2004 has unique values Unique
Employed_2004 has unique values Unique
Civilian_labor_force_2005 has unique values Unique
Employed_2005 has unique values Unique
Unemployed_2005 has unique values Unique
Civilian_labor_force_2006 has unique values Unique
Employed_2006 has unique values Unique
Unemployed_2006 has unique values Unique
Civilian_labor_force_2007 has unique values Unique
Employed_2007 has unique values Unique
Unemployed_2007 has unique values Unique
Civilian_labor_force_2008 has unique values Unique
Employed_2008 has unique values Unique
Civilian_labor_force_2009 has unique values Unique
Employed_2009 has unique values Unique
Unemployed_2009 has unique values Unique
Civilian_labor_force_2010 has unique values Unique
Employed_2010 has unique values Unique
Unemployed_2010 has unique values Unique
Civilian_labor_force_2011 has unique values Unique
Employed_2011 has unique values Unique
Unemployed_2011 has unique values Unique
Civilian_labor_force_2012 has unique values Unique
Employed_2012 has unique values Unique
Unemployed_2012 has unique values Unique
Civilian_labor_force_2013 has unique values Unique
Employed_2013 has unique values Unique
Unemployed_2013 has unique values Unique
Civilian_labor_force_2014 has unique values Unique
Employed_2014 has unique values Unique
Unemployed_2014 has unique values Unique
Civilian_labor_force_2015 has unique values Unique
Employed_2015 has unique values Unique
Unemployed_2015 has unique values Unique
Civilian_labor_force_2016 has unique values Unique
Unemployed_2016 has unique values Unique
Civilian_labor_force_2017 has unique values Unique
Employed_2017 has unique values Unique
Unemployed_2017 has unique values Unique
Civilian_labor_force_2018 has unique values Unique
Employed_2018 has unique values Unique
Unemployed_2018 has unique values Unique
Civilian_labor_force_2019 has unique values Unique
Employed_2019 has unique values Unique
Median_Household_Income_2018 has unique values Unique

Reproduction

Analysis started2021-01-15 02:54:54.851811
Analysis finished2021-01-15 02:59:11.233314
Duration4 minutes and 16.38 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1894.5
Minimum1864
Maximum1925
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:11.516297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1864
5-th percentile1867.05
Q11879.25
median1894.5
Q31909.75
95-th percentile1921.95
Maximum1925
Range61
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation18.04161855
Coefficient of variation (CV)0.009523155742
Kurtosis-1.2
Mean1894.5
Median Absolute Deviation (MAD)15.5
Skewness0
Sum117459
Variance325.5
MonotocityStrictly increasing
2021-01-15T02:59:11.806599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19191
 
1.6%
18731
 
1.6%
18861
 
1.6%
18851
 
1.6%
18841
 
1.6%
18831
 
1.6%
18821
 
1.6%
18811
 
1.6%
18801
 
1.6%
18791
 
1.6%
Other values (52)52
83.9%
ValueCountFrequency (%)
18641
1.6%
18651
1.6%
18661
1.6%
18671
1.6%
18681
1.6%
ValueCountFrequency (%)
19251
1.6%
19241
1.6%
19231
1.6%
19221
1.6%
19211
1.6%

FIPStxt
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36062
Minimum36001
Maximum36123
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:12.100463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum36001
5-th percentile36007.1
Q136031.5
median36062
Q336092.5
95-th percentile36116.9
Maximum36123
Range122
Interquartile range (IQR)61

Descriptive statistics

Standard deviation36.08323711
Coefficient of variation (CV)0.001000588905
Kurtosis-1.2
Mean36062
Median Absolute Deviation (MAD)31
Skewness0
Sum2235844
Variance1302
MonotocityStrictly increasing
2021-01-15T02:59:12.409490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360951
 
1.6%
360031
 
1.6%
360291
 
1.6%
360271
 
1.6%
360251
 
1.6%
360231
 
1.6%
360211
 
1.6%
360191
 
1.6%
360171
 
1.6%
360151
 
1.6%
Other values (52)52
83.9%
ValueCountFrequency (%)
360011
1.6%
360031
1.6%
360051
1.6%
360071
1.6%
360091
1.6%
ValueCountFrequency (%)
361231
1.6%
361211
1.6%
361191
1.6%
361171
1.6%
361151
1.6%

Stabr
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size624.0 B
NY
62 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters124
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowNY
3rd rowNY
4th rowNY
5th rowNY
ValueCountFrequency (%)
NY62
100.0%
2021-01-15T02:59:12.893595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T02:59:13.036229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny62
100.0%

Most occurring characters

ValueCountFrequency (%)
N62
50.0%
Y62
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter124
100.0%

Most frequent character per category

ValueCountFrequency (%)
N62
50.0%
Y62
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124
100.0%

Most frequent character per script

ValueCountFrequency (%)
N62
50.0%
Y62
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII124
100.0%

Most frequent character per block

ValueCountFrequency (%)
N62
50.0%
Y62
50.0%

area_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
Putnam County, NY
 
1
Rensselaer County, NY
 
1
Delaware County, NY
 
1
Oswego County, NY
 
1
Bronx County, NY
 
1
Other values (57)
57 

Length

Max length23
Median length18
Mean length18.32258065
Min length15

Characters and Unicode

Total characters1136
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st rowAlbany County, NY
2nd rowAllegany County, NY
3rd rowBronx County, NY
4th rowBroome County, NY
5th rowCattaraugus County, NY
ValueCountFrequency (%)
Putnam County, NY1
 
1.6%
Rensselaer County, NY1
 
1.6%
Delaware County, NY1
 
1.6%
Oswego County, NY1
 
1.6%
Bronx County, NY1
 
1.6%
Saratoga County, NY1
 
1.6%
Westchester County, NY1
 
1.6%
New York County, NY1
 
1.6%
Tompkins County, NY1
 
1.6%
Nassau County, NY1
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:13.518353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county62
33.0%
ny62
33.0%
wyoming1
 
0.5%
kings1
 
0.5%
cortland1
 
0.5%
niagara1
 
0.5%
ulster1
 
0.5%
schoharie1
 
0.5%
seneca1
 
0.5%
rockland1
 
0.5%
Other values (56)56
29.8%

Most occurring characters

ValueCountFrequency (%)
126
 
11.1%
n106
 
9.3%
o92
 
8.1%
t84
 
7.4%
u79
 
7.0%
y70
 
6.2%
C70
 
6.2%
N65
 
5.7%
Y64
 
5.6%
,62
 
5.5%
Other values (39)318
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter697
61.4%
Uppercase Letter250
 
22.0%
Space Separator126
 
11.1%
Other Punctuation63
 
5.5%

Most frequent character per category

ValueCountFrequency (%)
n106
15.2%
o92
13.2%
t84
12.1%
u79
11.3%
y70
10.0%
e51
7.3%
a45
6.5%
r27
 
3.9%
s25
 
3.6%
i21
 
3.0%
Other values (14)97
13.9%
ValueCountFrequency (%)
C70
28.0%
N65
26.0%
Y64
25.6%
S9
 
3.6%
O7
 
2.8%
W5
 
2.0%
L3
 
1.2%
M3
 
1.2%
R3
 
1.2%
A2
 
0.8%
Other values (12)19
 
7.6%
ValueCountFrequency (%)
,62
98.4%
.1
 
1.6%
ValueCountFrequency (%)
126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin947
83.4%
Common189
 
16.6%

Most frequent character per script

ValueCountFrequency (%)
n106
11.2%
o92
9.7%
t84
 
8.9%
u79
 
8.3%
y70
 
7.4%
C70
 
7.4%
N65
 
6.9%
Y64
 
6.8%
e51
 
5.4%
a45
 
4.8%
Other values (36)221
23.3%
ValueCountFrequency (%)
126
66.7%
,62
32.8%
.1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1136
100.0%

Most frequent character per block

ValueCountFrequency (%)
126
 
11.1%
n106
 
9.3%
o92
 
8.1%
t84
 
7.4%
u79
 
7.0%
y70
 
6.2%
C70
 
6.2%
N65
 
5.7%
Y64
 
5.6%
,62
 
5.5%
Other values (39)318
28.0%

Rural_urban_continuum_code_2013
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.064516129
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:13.754481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6.95
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.063507818
Coefficient of variation (CV)0.6733551828
Kurtosis-0.777290409
Mean3.064516129
Median Absolute Deviation (MAD)1
Skewness0.6857869227
Sum190
Variance4.258064516
MonotocityNot monotonic
2021-01-15T02:59:13.954740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
120
32.3%
212
19.4%
410
16.1%
69
14.5%
36
 
9.7%
73
 
4.8%
81
 
1.6%
51
 
1.6%
ValueCountFrequency (%)
120
32.3%
212
19.4%
36
 
9.7%
410
16.1%
51
 
1.6%
ValueCountFrequency (%)
81
 
1.6%
73
 
4.8%
69
14.5%
51
 
1.6%
410
16.1%

Urban_influence_code_2013
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.967741935
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:14.173162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34.75
95-th percentile7.95
Maximum9
Range8
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.24680277
Coefficient of variation (CV)0.7570748463
Kurtosis0.113538064
Mean2.967741935
Median Absolute Deviation (MAD)1
Skewness1.116869787
Sum184
Variance5.048122686
MonotocityNot monotonic
2021-01-15T02:59:14.376923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
120
32.3%
218
29.0%
36
 
9.7%
65
 
8.1%
55
 
8.1%
83
 
4.8%
42
 
3.2%
72
 
3.2%
91
 
1.6%
ValueCountFrequency (%)
120
32.3%
218
29.0%
36
 
9.7%
42
 
3.2%
55
 
8.1%
ValueCountFrequency (%)
91
 
1.6%
83
4.8%
72
 
3.2%
65
8.1%
55
8.1%

Metro_2013
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size624.0 B
1.0
38 
0.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.038
61.3%
0.024
38.7%
2021-01-15T02:59:14.879538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T02:59:15.018860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.038
61.3%
0.024
38.7%

Most occurring characters

ValueCountFrequency (%)
086
46.2%
.62
33.3%
138
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124
66.7%
Other Punctuation62
33.3%

Most frequent character per category

ValueCountFrequency (%)
086
69.4%
138
30.6%
ValueCountFrequency (%)
.62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common186
100.0%

Most frequent character per script

ValueCountFrequency (%)
086
46.2%
.62
33.3%
138
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII186
100.0%

Most frequent character per block

ValueCountFrequency (%)
086
46.2%
.62
33.3%
138
20.4%

Civilian_labor_force_2000
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
58,432
 
1
109,413
 
1
31,544
 
1
42,544
 
1
80,419
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row153,997
2nd row22,659
3rd row486,621
4th row97,734
5th row40,867
ValueCountFrequency (%)
58,4321
 
1.6%
109,4131
 
1.6%
31,5441
 
1.6%
42,5441
 
1.6%
80,4191
 
1.6%
31,3441
 
1.6%
47,8111
 
1.6%
54,3091
 
1.6%
110,4071
 
1.6%
26,3981
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:15.518182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58,4321
 
1.6%
109,4131
 
1.6%
31,5441
 
1.6%
42,5441
 
1.6%
80,4191
 
1.6%
31,3441
 
1.6%
47,8111
 
1.6%
54,3091
 
1.6%
110,4071
 
1.6%
26,3981
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
255
14.0%
444
11.2%
141
10.5%
339
9.9%
727
6.9%
827
6.9%
524
 
6.1%
924
 
6.1%
024
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
255
16.8%
444
13.4%
141
12.5%
339
11.9%
727
8.2%
827
8.2%
524
7.3%
924
7.3%
024
7.3%
623
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
255
14.0%
444
11.2%
141
10.5%
339
9.9%
727
6.9%
827
6.9%
524
 
6.1%
924
 
6.1%
024
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
255
14.0%
444
11.2%
141
10.5%
339
9.9%
727
6.9%
827
6.9%
524
 
6.1%
924
 
6.1%
024
 
6.1%

Employed_2000
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
21,544
 
1
69,694
 
1
23,390
 
1
25,482
 
1
40,695
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row148,813
2nd row21,544
3rd row452,036
4th row94,155
5th row39,002
ValueCountFrequency (%)
21,5441
 
1.6%
69,6941
 
1.6%
23,3901
 
1.6%
25,4821
 
1.6%
40,6951
 
1.6%
15,6591
 
1.6%
812,2371
 
1.6%
17,5541
 
1.6%
104,2501
 
1.6%
706,1671
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:16.105417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,5441
 
1.6%
69,6941
 
1.6%
23,3901
 
1.6%
25,4821
 
1.6%
40,6951
 
1.6%
15,6591
 
1.6%
812,2371
 
1.6%
17,5541
 
1.6%
104,2501
 
1.6%
706,1671
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,63
16.2%
146
11.8%
244
11.3%
437
9.5%
335
9.0%
535
9.0%
932
8.2%
630
7.7%
829
7.4%
025
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.8%
Other Punctuation63
 
16.2%

Most frequent character per category

ValueCountFrequency (%)
146
14.1%
244
13.5%
437
11.3%
335
10.7%
535
10.7%
932
9.8%
630
9.2%
829
8.9%
025
7.6%
714
 
4.3%
ValueCountFrequency (%)
,63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,63
16.2%
146
11.8%
244
11.3%
437
9.5%
335
9.0%
535
9.0%
932
8.2%
630
7.7%
829
7.4%
025
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,63
16.2%
146
11.8%
244
11.3%
437
9.5%
335
9.0%
535
9.0%
932
8.2%
630
7.7%
829
7.4%
025
 
6.4%

Unemployed_2000
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
983
 
1
4,318
 
1
947
 
1
962
 
1
8,125
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.774193548
Min length3

Characters and Unicode

Total characters296
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row5,184
2nd row1,115
3rd row34,585
4th row3,579
5th row1,865
ValueCountFrequency (%)
9831
 
1.6%
4,3181
 
1.6%
9471
 
1.6%
9621
 
1.6%
8,1251
 
1.6%
5,1841
 
1.6%
1,2611
 
1.6%
19,6821
 
1.6%
1,8641
 
1.6%
8881
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:16.674045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9831
 
1.6%
4,3181
 
1.6%
9471
 
1.6%
9621
 
1.6%
8,1251
 
1.6%
5,1841
 
1.6%
1,2611
 
1.6%
19,6821
 
1.6%
1,8641
 
1.6%
8881
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
154
18.2%
,50
16.9%
529
9.8%
229
9.8%
825
8.4%
422
7.4%
621
 
7.1%
320
 
6.8%
919
 
6.4%
715
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number246
83.1%
Other Punctuation50
 
16.9%

Most frequent character per category

ValueCountFrequency (%)
154
22.0%
529
11.8%
229
11.8%
825
10.2%
422
8.9%
621
 
8.5%
320
 
8.1%
919
 
7.7%
715
 
6.1%
012
 
4.9%
ValueCountFrequency (%)
,50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common296
100.0%

Most frequent character per script

ValueCountFrequency (%)
154
18.2%
,50
16.9%
529
9.8%
229
9.8%
825
8.4%
422
7.4%
621
 
7.1%
320
 
6.8%
919
 
6.4%
715
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII296
100.0%

Most frequent character per block

ValueCountFrequency (%)
154
18.2%
,50
16.9%
529
9.8%
229
9.8%
825
8.4%
422
7.4%
621
 
7.1%
320
 
6.8%
919
 
6.4%
715
 
5.1%

Unemployment_rate_2000
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.267741935
Minimum2.9
Maximum7.1
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:16.918168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.3
Q13.6
median4.2
Q34.7
95-th percentile5.795
Maximum7.1
Range4.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.8305191332
Coefficient of variation (CV)0.1946038785
Kurtosis1.287373498
Mean4.267741935
Median Absolute Deviation (MAD)0.6
Skewness1.015691681
Sum264.6
Variance0.6897620307
MonotocityNot monotonic
2021-01-15T02:59:17.148436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3.55
 
8.1%
4.84
 
6.5%
4.24
 
6.5%
4.34
 
6.5%
4.74
 
6.5%
3.64
 
6.5%
3.34
 
6.5%
4.13
 
4.8%
3.73
 
4.8%
3.93
 
4.8%
Other values (19)24
38.7%
ValueCountFrequency (%)
2.91
 
1.6%
3.21
 
1.6%
3.34
6.5%
3.43
4.8%
3.55
8.1%
ValueCountFrequency (%)
7.11
1.6%
6.31
1.6%
5.91
1.6%
5.81
1.6%
5.71
1.6%

Civilian_labor_force_2001
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
140,038
 
1
463,716
 
1
1,040,426
 
1
22,490
 
1
12,025
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row154,732
2nd row22,998
3rd row488,643
4th row98,088
5th row41,021
ValueCountFrequency (%)
140,0381
 
1.6%
463,7161
 
1.6%
1,040,4261
 
1.6%
22,4901
 
1.6%
12,0251
 
1.6%
1,063,7361
 
1.6%
163,8681
 
1.6%
80,7961
 
1.6%
108,4701
 
1.6%
26,2641
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:17.718843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
140,0381
 
1.6%
463,7161
 
1.6%
1,040,4261
 
1.6%
22,4901
 
1.6%
12,0251
 
1.6%
1,063,7361
 
1.6%
163,8681
 
1.6%
80,7961
 
1.6%
108,4701
 
1.6%
26,2641
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
244
11.2%
340
10.2%
138
9.7%
438
9.7%
637
9.4%
834
8.7%
032
8.2%
524
 
6.1%
721
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
244
13.4%
340
12.2%
138
11.6%
438
11.6%
637
11.3%
834
10.4%
032
9.8%
524
7.3%
721
6.4%
920
6.1%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
244
11.2%
340
10.2%
138
9.7%
438
9.7%
637
9.4%
834
8.7%
032
8.2%
524
 
6.1%
721
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
244
11.2%
340
10.2%
138
9.7%
438
9.7%
637
9.4%
834
8.7%
032
8.2%
524
 
6.1%
721
 
5.4%

Employed_2001
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
77,726
 
1
50,839
 
1
31,262
 
1
446,213
 
1
30,034
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row149,564
2nd row21,908
3rd row453,138
4th row93,925
5th row38,959
ValueCountFrequency (%)
77,7261
 
1.6%
50,8391
 
1.6%
31,2621
 
1.6%
446,2131
 
1.6%
30,0341
 
1.6%
11,7321
 
1.6%
37,0421
 
1.6%
31,8091
 
1.6%
453,1381
 
1.6%
45,9381
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:18.307746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
77,7261
 
1.6%
50,8391
 
1.6%
31,2621
 
1.6%
446,2131
 
1.6%
30,0341
 
1.6%
11,7321
 
1.6%
37,0421
 
1.6%
31,8091
 
1.6%
453,1381
 
1.6%
45,9381
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,63
16.2%
246
11.8%
140
10.3%
338
9.7%
435
9.0%
931
7.9%
531
7.9%
831
7.9%
730
7.7%
029
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.8%
Other Punctuation63
 
16.2%

Most frequent character per category

ValueCountFrequency (%)
246
14.1%
140
12.2%
338
11.6%
435
10.7%
931
9.5%
531
9.5%
831
9.5%
730
9.2%
029
8.9%
616
 
4.9%
ValueCountFrequency (%)
,63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,63
16.2%
246
11.8%
140
10.3%
338
9.7%
435
9.0%
931
7.9%
531
7.9%
831
7.9%
730
7.7%
029
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,63
16.2%
246
11.8%
140
10.3%
338
9.7%
435
9.0%
931
7.9%
531
7.9%
831
7.9%
730
7.7%
029
7.4%

Unemployed_2001
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
706
 
1
1,023
 
1
647
 
1
1,725
 
1
1,391
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.870967742
Min length3

Characters and Unicode

Total characters302
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row5,168
2nd row1,090
3rd row35,505
4th row4,163
5th row2,062
ValueCountFrequency (%)
7061
 
1.6%
1,0231
 
1.6%
6471
 
1.6%
1,7251
 
1.6%
1,3911
 
1.6%
1,0161
 
1.6%
1,1771
 
1.6%
1,5771
 
1.6%
17,5031
 
1.6%
3,3411
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:18.879884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7061
 
1.6%
1,0231
 
1.6%
6471
 
1.6%
1,7251
 
1.6%
1,3911
 
1.6%
1,0161
 
1.6%
1,1771
 
1.6%
1,5771
 
1.6%
17,5031
 
1.6%
3,3411
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
154
17.9%
,53
17.5%
026
8.6%
326
8.6%
226
8.6%
522
7.3%
420
 
6.6%
720
 
6.6%
919
 
6.3%
618
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number249
82.5%
Other Punctuation53
 
17.5%

Most frequent character per category

ValueCountFrequency (%)
154
21.7%
026
10.4%
326
10.4%
226
10.4%
522
8.8%
420
 
8.0%
720
 
8.0%
919
 
7.6%
618
 
7.2%
818
 
7.2%
ValueCountFrequency (%)
,53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common302
100.0%

Most frequent character per script

ValueCountFrequency (%)
154
17.9%
,53
17.5%
026
8.6%
326
8.6%
226
8.6%
522
7.3%
420
 
6.6%
720
 
6.6%
919
 
6.3%
618
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII302
100.0%

Most frequent character per block

ValueCountFrequency (%)
154
17.9%
,53
17.5%
026
8.6%
326
8.6%
226
8.6%
522
7.3%
420
 
6.6%
720
 
6.6%
919
 
6.3%
618
 
6.0%

Unemployment_rate_2001
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.530645161
Minimum3.2
Maximum7.3
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:19.113444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile3.4
Q14
median4.5
Q34.9
95-th percentile6.1
Maximum7.3
Range4.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.847482039
Coefficient of variation (CV)0.1870554874
Kurtosis1.022941098
Mean4.530645161
Median Absolute Deviation (MAD)0.5
Skewness0.894283508
Sum280.9
Variance0.7182258065
MonotocityNot monotonic
2021-01-15T02:59:19.348361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4.58
 
12.9%
3.85
 
8.1%
4.13
 
4.8%
4.23
 
4.8%
3.43
 
4.8%
4.33
 
4.8%
4.73
 
4.8%
43
 
4.8%
4.93
 
4.8%
4.63
 
4.8%
Other values (17)25
40.3%
ValueCountFrequency (%)
3.21
 
1.6%
3.32
3.2%
3.43
4.8%
3.52
3.2%
3.72
3.2%
ValueCountFrequency (%)
7.31
1.6%
6.41
1.6%
6.31
1.6%
6.12
3.2%
61
1.6%

Civilian_labor_force_2002
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
115,221
 
1
49,830
 
1
52,573
 
1
45,880
 
1
82,208
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row157,694
2nd row23,449
3rd row496,904
4th row98,781
5th row41,755
ValueCountFrequency (%)
115,2211
 
1.6%
49,8301
 
1.6%
52,5731
 
1.6%
45,8801
 
1.6%
82,2081
 
1.6%
17,0661
 
1.6%
471,2461
 
1.6%
18,7821
 
1.6%
47,1051
 
1.6%
42,2691
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:19.927448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
115,2211
 
1.6%
49,8301
 
1.6%
52,5731
 
1.6%
45,8801
 
1.6%
82,2081
 
1.6%
17,0661
 
1.6%
471,2461
 
1.6%
18,7821
 
1.6%
47,1051
 
1.6%
42,2691
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
146
11.7%
245
11.5%
434
8.7%
334
8.7%
031
7.9%
529
7.4%
929
7.4%
628
7.1%
726
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
146
14.0%
245
13.7%
434
10.4%
334
10.4%
031
9.5%
529
8.8%
929
8.8%
628
8.5%
726
7.9%
826
7.9%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
146
11.7%
245
11.5%
434
8.7%
334
8.7%
031
7.9%
529
7.4%
929
7.4%
628
7.1%
726
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
146
11.7%
245
11.5%
434
8.7%
334
8.7%
031
7.9%
529
7.4%
929
7.4%
628
7.1%
726
6.6%

Employed_2002
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
23,259
 
1
31,361
 
1
718,973
 
1
11,975
 
1
17,820
 
1
Other values (57)
57 

Length

Max length7
Median length6
Mean length6.258064516
Min length5

Characters and Unicode

Total characters388
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,536
2nd row22,192
3rd row448,504
4th row93,088
5th row39,427
ValueCountFrequency (%)
23,2591
 
1.6%
31,3611
 
1.6%
718,9731
 
1.6%
11,9751
 
1.6%
17,8201
 
1.6%
105,1571
 
1.6%
51,6961
 
1.6%
86,8171
 
1.6%
21,9781
 
1.6%
29,8261
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:20.520652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23,2591
 
1.6%
31,3611
 
1.6%
718,9731
 
1.6%
11,9751
 
1.6%
17,8201
 
1.6%
105,1571
 
1.6%
51,6961
 
1.6%
86,8171
 
1.6%
21,9781
 
1.6%
29,8261
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,62
16.0%
244
11.3%
139
10.1%
535
9.0%
035
9.0%
331
8.0%
431
8.0%
928
7.2%
828
7.2%
728
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
84.0%
Other Punctuation62
 
16.0%

Most frequent character per category

ValueCountFrequency (%)
244
13.5%
139
12.0%
535
10.7%
035
10.7%
331
9.5%
431
9.5%
928
8.6%
828
8.6%
728
8.6%
627
8.3%
ValueCountFrequency (%)
,62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common388
100.0%

Most frequent character per script

ValueCountFrequency (%)
,62
16.0%
244
11.3%
139
10.1%
535
9.0%
035
9.0%
331
8.0%
431
8.0%
928
7.2%
828
7.2%
728
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII388
100.0%

Most frequent character per block

ValueCountFrequency (%)
,62
16.0%
244
11.3%
139
10.1%
535
9.0%
035
9.0%
331
8.0%
431
8.0%
928
7.2%
828
7.2%
728
7.2%

Unemployed_2002
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct60
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,330
 
2
1,455
 
2
3,973
 
1
3,268
 
1
2,108
 
1
Other values (55)
55 

Length

Max length6
Median length5
Mean length4.951612903
Min length3

Characters and Unicode

Total characters307
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)93.5%

Sample

1st row6,158
2nd row1,257
3rd row48,400
4th row5,693
5th row2,328
ValueCountFrequency (%)
1,3302
 
3.2%
1,4552
 
3.2%
3,9731
 
1.6%
3,2681
 
1.6%
2,1081
 
1.6%
75,8751
 
1.6%
5,6931
 
1.6%
6,3841
 
1.6%
1,3471
 
1.6%
1,4571
 
1.6%
Other values (50)50
80.6%
2021-01-15T02:59:21.101616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,3302
 
3.2%
1,4552
 
3.2%
3,9731
 
1.6%
3,2681
 
1.6%
2,1081
 
1.6%
75,8751
 
1.6%
5,6931
 
1.6%
6,3841
 
1.6%
1,3471
 
1.6%
1,4571
 
1.6%
Other values (50)50
80.6%

Most occurring characters

ValueCountFrequency (%)
,55
17.9%
139
12.7%
333
10.7%
226
8.5%
725
8.1%
025
8.1%
624
7.8%
524
7.8%
921
 
6.8%
820
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
82.1%
Other Punctuation55
 
17.9%

Most frequent character per category

ValueCountFrequency (%)
139
15.5%
333
13.1%
226
10.3%
725
9.9%
025
9.9%
624
9.5%
524
9.5%
921
8.3%
820
7.9%
415
 
6.0%
ValueCountFrequency (%)
,55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common307
100.0%

Most frequent character per script

ValueCountFrequency (%)
,55
17.9%
139
12.7%
333
10.7%
226
8.5%
725
8.1%
025
8.1%
624
7.8%
524
7.8%
921
 
6.8%
820
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII307
100.0%

Most frequent character per block

ValueCountFrequency (%)
,55
17.9%
139
12.7%
333
10.7%
226
8.5%
725
8.1%
025
8.1%
624
7.8%
524
7.8%
921
 
6.8%
820
 
6.5%

Unemployment_rate_2002
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.453225806
Minimum3.8
Maximum9.7
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:21.326294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.005
Q14.7
median5.3
Q35.975
95-th percentile7.1
Maximum9.7
Range5.9
Interquartile range (IQR)1.275

Descriptive statistics

Standard deviation1.121286986
Coefficient of variation (CV)0.2056190274
Kurtosis2.762060408
Mean5.453225806
Median Absolute Deviation (MAD)0.65
Skewness1.303055644
Sum338.1
Variance1.257284506
MonotocityNot monotonic
2021-01-15T02:59:21.564636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
5.35
 
8.1%
4.94
 
6.5%
4.63
 
4.8%
5.43
 
4.8%
5.73
 
4.8%
5.53
 
4.8%
5.62
 
3.2%
6.32
 
3.2%
4.32
 
3.2%
4.72
 
3.2%
Other values (23)33
53.2%
ValueCountFrequency (%)
3.81
1.6%
3.92
3.2%
41
1.6%
4.11
1.6%
4.22
3.2%
ValueCountFrequency (%)
9.71
1.6%
8.61
1.6%
7.61
1.6%
7.12
3.2%
6.92
3.2%

Civilian_labor_force_2003
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
12,435
 
1
91,397
 
1
231,168
 
1
54,374
 
1
15,464
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row157,860
2nd row23,399
3rd row497,689
4th row96,722
5th row42,157
ValueCountFrequency (%)
12,4351
 
1.6%
91,3971
 
1.6%
231,1681
 
1.6%
54,3741
 
1.6%
15,4641
 
1.6%
59,8801
 
1.6%
21,0911
 
1.6%
472,0451
 
1.6%
23,3991
 
1.6%
148,4011
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:22.159732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,4351
 
1.6%
91,3971
 
1.6%
231,1681
 
1.6%
54,3741
 
1.6%
15,4641
 
1.6%
59,8801
 
1.6%
21,0911
 
1.6%
472,0451
 
1.6%
23,3991
 
1.6%
148,4011
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
150
12.8%
342
10.7%
238
9.7%
435
8.9%
731
7.9%
931
7.9%
828
7.1%
526
6.6%
624
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
150
15.2%
342
12.8%
238
11.6%
435
10.7%
731
9.5%
931
9.5%
828
8.5%
526
7.9%
624
7.3%
023
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
150
12.8%
342
10.7%
238
9.7%
435
8.9%
731
7.9%
931
7.9%
828
7.1%
526
6.6%
624
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
150
12.8%
342
10.7%
238
9.7%
435
8.9%
731
7.9%
931
7.9%
828
7.1%
526
6.6%
624
 
6.1%

Employed_2003
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
21,909
 
1
19,821
 
1
55,518
 
1
24,842
 
1
450,950
 
1
Other values (57)
57 

Length

Max length7
Median length6
Mean length6.258064516
Min length5

Characters and Unicode

Total characters388
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,200
2nd row21,881
3rd row444,901
4th row91,138
5th row39,725
ValueCountFrequency (%)
21,9091
 
1.6%
19,8211
 
1.6%
55,5181
 
1.6%
24,8421
 
1.6%
450,9501
 
1.6%
22,4761
 
1.6%
104,7031
 
1.6%
32,7291
 
1.6%
12,2701
 
1.6%
11,6391
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:22.748134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,9091
 
1.6%
19,8211
 
1.6%
55,5181
 
1.6%
24,8421
 
1.6%
450,9501
 
1.6%
22,4761
 
1.6%
104,7031
 
1.6%
32,7291
 
1.6%
12,2701
 
1.6%
11,6391
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,62
16.0%
249
12.6%
142
10.8%
938
9.8%
536
9.3%
433
8.5%
030
7.7%
330
7.7%
726
6.7%
824
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
84.0%
Other Punctuation62
 
16.0%

Most frequent character per category

ValueCountFrequency (%)
249
15.0%
142
12.9%
938
11.7%
536
11.0%
433
10.1%
030
9.2%
330
9.2%
726
8.0%
824
7.4%
618
 
5.5%
ValueCountFrequency (%)
,62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common388
100.0%

Most frequent character per script

ValueCountFrequency (%)
,62
16.0%
249
12.6%
142
10.8%
938
9.8%
536
9.3%
433
8.5%
030
7.7%
330
7.7%
726
6.7%
824
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII388
100.0%

Most frequent character per block

ValueCountFrequency (%)
,62
16.0%
249
12.6%
142
10.8%
938
9.8%
536
9.3%
433
8.5%
030
7.7%
330
7.7%
726
6.7%
824
 
6.2%

Unemployed_2003
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,377
 
1
1,774
 
1
2,840
 
1
3,453
 
1
36,295
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.951612903
Min length3

Characters and Unicode

Total characters307
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,660
2nd row1,518
3rd row52,788
4th row5,584
5th row2,432
ValueCountFrequency (%)
1,3771
 
1.6%
1,7741
 
1.6%
2,8401
 
1.6%
3,4531
 
1.6%
36,2951
 
1.6%
8,0611
 
1.6%
3,8261
 
1.6%
1,4241
 
1.6%
11,6821
 
1.6%
52,7881
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:23.318528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,3771
 
1.6%
1,7741
 
1.6%
2,8401
 
1.6%
3,4531
 
1.6%
36,2951
 
1.6%
8,0611
 
1.6%
3,8261
 
1.6%
1,4241
 
1.6%
11,6821
 
1.6%
52,7881
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,55
17.9%
142
13.7%
628
9.1%
228
9.1%
826
8.5%
325
8.1%
923
7.5%
421
 
6.8%
020
 
6.5%
520
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
82.1%
Other Punctuation55
 
17.9%

Most frequent character per category

ValueCountFrequency (%)
142
16.7%
628
11.1%
228
11.1%
826
10.3%
325
9.9%
923
9.1%
421
8.3%
020
7.9%
520
7.9%
719
7.5%
ValueCountFrequency (%)
,55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common307
100.0%

Most frequent character per script

ValueCountFrequency (%)
,55
17.9%
142
13.7%
628
9.1%
228
9.1%
826
8.5%
325
8.1%
923
7.5%
421
 
6.8%
020
 
6.5%
520
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII307
100.0%

Most frequent character per block

ValueCountFrequency (%)
,55
17.9%
142
13.7%
628
9.1%
228
9.1%
826
8.5%
325
8.1%
923
7.5%
421
 
6.8%
020
 
6.5%
520
 
6.5%

Unemployment_rate_2003
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.683870968
Minimum3.9
Maximum10.6
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:23.545678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile4.105
Q14.8
median5.5
Q36.4
95-th percentile7.495
Maximum10.6
Range6.7
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.223833476
Coefficient of variation (CV)0.2153168998
Kurtosis3.564403845
Mean5.683870968
Median Absolute Deviation (MAD)0.75
Skewness1.403816151
Sum352.4
Variance1.497768377
MonotocityNot monotonic
2021-01-15T02:59:23.781527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6.44
 
6.5%
5.84
 
6.5%
4.54
 
6.5%
4.84
 
6.5%
5.74
 
6.5%
5.24
 
6.5%
5.43
 
4.8%
5.53
 
4.8%
4.63
 
4.8%
5.13
 
4.8%
Other values (20)26
41.9%
ValueCountFrequency (%)
3.92
3.2%
41
1.6%
4.11
1.6%
4.21
1.6%
4.31
1.6%
ValueCountFrequency (%)
10.61
1.6%
9.11
1.6%
7.52
3.2%
7.41
1.6%
7.32
3.2%

Civilian_labor_force_2004
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
49,052
 
1
768,946
 
1
24,075
 
1
19,858
 
1
91,379
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,668
2nd row23,578
3rd row502,064
4th row96,283
5th row42,270
ValueCountFrequency (%)
49,0521
 
1.6%
768,9461
 
1.6%
24,0751
 
1.6%
19,8581
 
1.6%
91,3791
 
1.6%
26,1401
 
1.6%
55,3191
 
1.6%
55,5641
 
1.6%
17,2211
 
1.6%
13,0571
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:24.364765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
49,0521
 
1.6%
768,9461
 
1.6%
24,0751
 
1.6%
19,8581
 
1.6%
91,3791
 
1.6%
26,1401
 
1.6%
55,3191
 
1.6%
55,5641
 
1.6%
17,2211
 
1.6%
13,0571
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
144
11.2%
337
9.4%
235
8.9%
534
8.7%
433
8.4%
032
8.2%
631
7.9%
929
7.4%
828
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
144
13.4%
337
11.3%
235
10.7%
534
10.4%
433
10.1%
032
9.8%
631
9.5%
929
8.8%
828
8.5%
725
7.6%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
144
11.2%
337
9.4%
235
8.9%
534
8.7%
433
8.4%
032
8.2%
631
7.9%
929
7.4%
828
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
144
11.2%
337
9.4%
235
8.9%
534
8.7%
433
8.4%
032
8.2%
631
7.9%
929
7.4%
828
7.1%

Employed_2004
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
31,231
 
1
211,569
 
1
45,843
 
1
33,714
 
1
653,640
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row152,045
2nd row22,071
3rd row455,837
4th row91,075
5th row39,740
ValueCountFrequency (%)
31,2311
 
1.6%
211,5691
 
1.6%
45,8431
 
1.6%
33,7141
 
1.6%
653,6401
 
1.6%
143,1681
 
1.6%
30,5291
 
1.6%
22,7701
 
1.6%
104,7031
 
1.6%
30,8481
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:24.951634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31,2311
 
1.6%
211,5691
 
1.6%
45,8431
 
1.6%
33,7141
 
1.6%
653,6401
 
1.6%
143,1681
 
1.6%
30,5291
 
1.6%
22,7701
 
1.6%
104,7031
 
1.6%
30,8481
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,63
16.2%
145
11.5%
239
10.0%
736
9.2%
435
9.0%
335
9.0%
533
8.5%
030
7.7%
927
6.9%
824
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.8%
Other Punctuation63
 
16.2%

Most frequent character per category

ValueCountFrequency (%)
145
13.8%
239
11.9%
736
11.0%
435
10.7%
335
10.7%
533
10.1%
030
9.2%
927
8.3%
824
7.3%
623
7.0%
ValueCountFrequency (%)
,63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,63
16.2%
145
11.5%
239
10.0%
736
9.2%
435
9.0%
335
9.0%
533
8.5%
030
7.7%
927
6.9%
824
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,63
16.2%
145
11.5%
239
10.0%
736
9.2%
435
9.0%
335
9.0%
533
8.5%
030
7.7%
927
6.9%
824
 
6.2%

Unemployed_2004
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,507
 
2
14,514
 
1
822
 
1
1,524
 
1
1,435
 
1
Other values (56)
56 

Length

Max length6
Median length5
Mean length4.983870968
Min length3

Characters and Unicode

Total characters309
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)96.8%

Sample

1st row6,623
2nd row1,507
3rd row46,227
4th row5,208
5th row2,530
ValueCountFrequency (%)
1,5072
 
3.2%
14,5141
 
1.6%
8221
 
1.6%
1,5241
 
1.6%
1,4351
 
1.6%
6,1711
 
1.6%
1,8351
 
1.6%
5,7221
 
1.6%
1,4461
 
1.6%
8561
 
1.6%
Other values (51)51
82.3%
2021-01-15T02:59:25.549761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,5072
 
3.2%
14,5141
 
1.6%
8221
 
1.6%
1,5241
 
1.6%
1,4351
 
1.6%
6,1711
 
1.6%
1,8351
 
1.6%
5,7221
 
1.6%
1,4461
 
1.6%
8561
 
1.6%
Other values (51)51
82.3%

Most occurring characters

ValueCountFrequency (%)
,56
18.1%
142
13.6%
334
11.0%
233
10.7%
824
7.8%
623
7.4%
523
7.4%
722
 
7.1%
422
 
7.1%
020
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
81.9%
Other Punctuation56
 
18.1%

Most frequent character per category

ValueCountFrequency (%)
142
16.6%
334
13.4%
233
13.0%
824
9.5%
623
9.1%
523
9.1%
722
8.7%
422
8.7%
020
7.9%
910
 
4.0%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common309
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.1%
142
13.6%
334
11.0%
233
10.7%
824
7.8%
623
7.4%
523
7.4%
722
 
7.1%
422
 
7.1%
020
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII309
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.1%
142
13.6%
334
11.0%
233
10.7%
824
7.8%
623
7.4%
523
7.4%
722
 
7.1%
422
 
7.1%
020
 
6.5%

Unemployment_rate_2004
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.496774194
Minimum3.9
Maximum9.2
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:25.771486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile4.2
Q14.75
median5.5
Q36.15
95-th percentile6.9
Maximum9.2
Range5.3
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.9601857303
Coefficient of variation (CV)0.1746816763
Kurtosis2.373165295
Mean5.496774194
Median Absolute Deviation (MAD)0.7
Skewness0.9594317075
Sum340.8
Variance0.9219566367
MonotocityNot monotonic
2021-01-15T02:59:26.024185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.57
 
11.3%
4.54
 
6.5%
5.24
 
6.5%
6.44
 
6.5%
6.24
 
6.5%
63
 
4.8%
5.63
 
4.8%
4.43
 
4.8%
4.93
 
4.8%
4.73
 
4.8%
Other values (17)24
38.7%
ValueCountFrequency (%)
3.91
 
1.6%
42
3.2%
4.22
3.2%
4.43
4.8%
4.54
6.5%
ValueCountFrequency (%)
9.21
1.6%
7.61
1.6%
71
1.6%
6.92
3.2%
6.52
3.2%

Civilian_labor_force_2005
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
42,123
 
1
32,873
 
1
40,595
 
1
902,717
 
1
75,405
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row159,258
2nd row23,532
3rd row507,229
4th row96,287
5th row42,123
ValueCountFrequency (%)
42,1231
 
1.6%
32,8731
 
1.6%
40,5951
 
1.6%
902,7171
 
1.6%
75,4051
 
1.6%
1,090,0281
 
1.6%
233,6891
 
1.6%
31,9421
 
1.6%
177,5961
 
1.6%
507,2291
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:26.603443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
42,1231
 
1.6%
32,8731
 
1.6%
40,5951
 
1.6%
902,7171
 
1.6%
75,4051
 
1.6%
1,090,0281
 
1.6%
233,6891
 
1.6%
31,9421
 
1.6%
177,5961
 
1.6%
507,2291
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
152
13.3%
241
10.5%
537
9.4%
332
8.2%
432
8.2%
931
7.9%
730
7.7%
825
 
6.4%
024
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
152
15.9%
241
12.5%
537
11.3%
332
9.8%
432
9.8%
931
9.5%
730
9.1%
825
7.6%
024
7.3%
624
7.3%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
152
13.3%
241
10.5%
537
9.4%
332
8.2%
432
8.2%
931
7.9%
730
7.7%
825
 
6.4%
024
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
152
13.3%
241
10.5%
537
9.4%
332
8.2%
432
8.2%
931
7.9%
730
7.7%
825
 
6.4%
024
 
6.1%

Employed_2005
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
661,861
 
1
105,942
 
1
745,077
 
1
23,092
 
1
63,929
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row153,040
2nd row22,163
3rd row468,809
4th row91,585
5th row39,858
ValueCountFrequency (%)
661,8611
 
1.6%
105,9421
 
1.6%
745,0771
 
1.6%
23,0921
 
1.6%
63,9291
 
1.6%
359,2881
 
1.6%
463,1881
 
1.6%
53,9841
 
1.6%
30,7081
 
1.6%
115,6031
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:27.198836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
661,8611
 
1.6%
105,9421
 
1.6%
745,0771
 
1.6%
23,0921
 
1.6%
63,9291
 
1.6%
359,2881
 
1.6%
463,1881
 
1.6%
53,9841
 
1.6%
30,7081
 
1.6%
115,6031
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
244
11.2%
343
11.0%
142
10.7%
534
8.7%
034
8.7%
831
7.9%
427
6.9%
926
6.6%
724
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
244
13.4%
343
13.1%
142
12.8%
534
10.4%
034
10.4%
831
9.5%
427
8.2%
926
7.9%
724
7.3%
623
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
244
11.2%
343
11.0%
142
10.7%
534
8.7%
034
8.7%
831
7.9%
427
6.9%
926
6.6%
724
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
244
11.2%
343
11.0%
142
10.7%
534
8.7%
034
8.7%
831
7.9%
427
6.9%
926
6.6%
724
 
6.1%

Unemployed_2005
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
32,595
 
1
66,843
 
1
1,435
 
1
4,702
 
1
1,314
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.983870968
Min length3

Characters and Unicode

Total characters309
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,218
2nd row1,369
3rd row38,420
4th row4,702
5th row2,265
ValueCountFrequency (%)
32,5951
 
1.6%
66,8431
 
1.6%
1,4351
 
1.6%
4,7021
 
1.6%
1,3141
 
1.6%
5591
 
1.6%
5,2911
 
1.6%
10,4891
 
1.6%
2,5511
 
1.6%
1,4991
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:27.772516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32,5951
 
1.6%
66,8431
 
1.6%
1,4351
 
1.6%
4,7021
 
1.6%
1,3141
 
1.6%
5591
 
1.6%
5,2911
 
1.6%
10,4891
 
1.6%
2,5511
 
1.6%
1,4991
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,56
18.1%
147
15.2%
236
11.7%
327
8.7%
925
8.1%
423
7.4%
522
 
7.1%
020
 
6.5%
619
 
6.1%
817
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
81.9%
Other Punctuation56
 
18.1%

Most frequent character per category

ValueCountFrequency (%)
147
18.6%
236
14.2%
327
10.7%
925
9.9%
423
9.1%
522
8.7%
020
7.9%
619
7.5%
817
 
6.7%
717
 
6.7%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common309
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.1%
147
15.2%
236
11.7%
327
8.7%
925
8.1%
423
7.4%
522
 
7.1%
020
 
6.5%
619
 
6.1%
817
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII309
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.1%
147
15.2%
236
11.7%
327
8.7%
925
8.1%
423
7.4%
522
 
7.1%
020
 
6.5%
619
 
6.1%
817
 
5.5%

Unemployment_rate_2005
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.935483871
Minimum3.6
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:27.996508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile3.9
Q14.425
median4.9
Q35.375
95-th percentile6.1
Maximum7.6
Range4
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.7592266933
Coefficient of variation (CV)0.1538302451
Kurtosis1.165201862
Mean4.935483871
Median Absolute Deviation (MAD)0.5
Skewness0.6081034998
Sum306
Variance0.5764251719
MonotocityNot monotonic
2021-01-15T02:59:28.229812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5.36
 
9.7%
4.85
 
8.1%
4.55
 
8.1%
4.24
 
6.5%
4.94
 
6.5%
43
 
4.8%
5.43
 
4.8%
5.23
 
4.8%
3.93
 
4.8%
5.12
 
3.2%
Other values (16)24
38.7%
ValueCountFrequency (%)
3.62
3.2%
3.71
 
1.6%
3.93
4.8%
43
4.8%
4.24
6.5%
ValueCountFrequency (%)
7.61
1.6%
6.22
3.2%
6.12
3.2%
61
1.6%
5.91
1.6%

Civilian_labor_force_2006
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
23,521
 
1
696,480
 
1
26,454
 
1
375,168
 
1
234,218
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row159,472
2nd row23,521
3rd row505,378
4th row96,998
5th row41,925
ValueCountFrequency (%)
23,5211
 
1.6%
696,4801
 
1.6%
26,4541
 
1.6%
375,1681
 
1.6%
234,2181
 
1.6%
471,1161
 
1.6%
31,9841
 
1.6%
55,3781
 
1.6%
67,1351
 
1.6%
56,8201
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:28.823480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23,5211
 
1.6%
696,4801
 
1.6%
26,4541
 
1.6%
375,1681
 
1.6%
234,2181
 
1.6%
471,1161
 
1.6%
31,9841
 
1.6%
55,3781
 
1.6%
67,1351
 
1.6%
56,8201
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
141
10.5%
341
10.5%
238
9.7%
434
8.7%
931
7.9%
631
7.9%
729
7.4%
829
7.4%
028
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
141
12.5%
341
12.5%
238
11.6%
434
10.4%
931
9.5%
631
9.5%
729
8.8%
829
8.8%
028
8.5%
526
7.9%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
141
10.5%
341
10.5%
238
9.7%
434
8.7%
931
7.9%
631
7.9%
729
7.4%
829
7.4%
028
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
141
10.5%
341
10.5%
238
9.7%
434
8.7%
931
7.9%
631
7.9%
729
7.4%
829
7.4%
028
7.1%

Employed_2006
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,016,247
 
1
358,950
 
1
73,050
 
1
756,494
 
1
23,542
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row153,385
2nd row22,306
3rd row471,748
4th row92,545
5th row39,788
ValueCountFrequency (%)
1,016,2471
 
1.6%
358,9501
 
1.6%
73,0501
 
1.6%
756,4941
 
1.6%
23,5421
 
1.6%
31,5501
 
1.6%
38,8791
 
1.6%
92,5451
 
1.6%
30,5851
 
1.6%
471,7481
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:29.411049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,016,2471
 
1.6%
358,9501
 
1.6%
73,0501
 
1.6%
756,4941
 
1.6%
23,5421
 
1.6%
31,5501
 
1.6%
38,8791
 
1.6%
92,5451
 
1.6%
30,5851
 
1.6%
471,7481
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
340
10.2%
440
10.2%
238
9.7%
537
9.4%
136
9.2%
633
8.4%
731
7.9%
027
6.9%
925
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
340
12.2%
440
12.2%
238
11.6%
537
11.3%
136
11.0%
633
10.1%
731
9.5%
027
8.2%
925
7.6%
821
6.4%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
340
10.2%
440
10.2%
238
9.7%
537
9.4%
136
9.2%
633
8.4%
731
7.9%
027
6.9%
925
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
340
10.2%
440
10.2%
238
9.7%
537
9.4%
136
9.2%
633
8.4%
731
7.9%
027
6.9%
925
 
6.4%

Unemployed_2006
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
499
 
1
1,421
 
1
5,595
 
1
704
 
1
6,087
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.967741935
Min length3

Characters and Unicode

Total characters308
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,087
2nd row1,215
3rd row33,630
4th row4,453
5th row2,137
ValueCountFrequency (%)
4991
 
1.6%
1,4211
 
1.6%
5,5951
 
1.6%
7041
 
1.6%
6,0871
 
1.6%
48,9391
 
1.6%
1,1981
 
1.6%
30,5721
 
1.6%
1,1291
 
1.6%
33,6301
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:29.981746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4991
 
1.6%
1,4211
 
1.6%
5,5951
 
1.6%
7041
 
1.6%
6,0871
 
1.6%
48,9391
 
1.6%
1,1981
 
1.6%
30,5721
 
1.6%
1,1291
 
1.6%
33,6301
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,56
18.2%
148
15.6%
334
11.0%
228
9.1%
527
8.8%
924
7.8%
722
 
7.1%
419
 
6.2%
018
 
5.8%
616
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
81.8%
Other Punctuation56
 
18.2%

Most frequent character per category

ValueCountFrequency (%)
148
19.0%
334
13.5%
228
11.1%
527
10.7%
924
9.5%
722
8.7%
419
 
7.5%
018
 
7.1%
616
 
6.3%
816
 
6.3%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.2%
148
15.6%
334
11.0%
228
9.1%
527
8.8%
924
7.8%
722
 
7.1%
419
 
6.2%
018
 
5.8%
616
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII308
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.2%
148
15.6%
334
11.0%
228
9.1%
527
8.8%
924
7.8%
722
 
7.1%
419
 
6.2%
018
 
5.8%
616
 
5.2%

Unemployment_rate_2006
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.653225806
Minimum3.4
Maximum6.7
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:30.206579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile3.7
Q14.225
median4.6
Q35.175
95-th percentile5.695
Maximum6.7
Range3.3
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.6679348505
Coefficient of variation (CV)0.1435423249
Kurtosis0.1386623145
Mean4.653225806
Median Absolute Deviation (MAD)0.5
Skewness0.3355191385
Sum288.5
Variance0.4461369646
MonotocityNot monotonic
2021-01-15T02:59:30.423601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.37
 
11.3%
4.56
 
9.7%
4.84
 
6.5%
5.44
 
6.5%
3.73
 
4.8%
53
 
4.8%
5.33
 
4.8%
5.23
 
4.8%
4.13
 
4.8%
3.83
 
4.8%
Other values (15)23
37.1%
ValueCountFrequency (%)
3.41
 
1.6%
3.52
3.2%
3.73
4.8%
3.83
4.8%
3.91
 
1.6%
ValueCountFrequency (%)
6.71
1.6%
5.81
1.6%
5.72
3.2%
5.61
1.6%
5.51
1.6%

Civilian_labor_force_2007
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,102,350
 
1
66,781
 
1
44,734
 
1
232,522
 
1
26,179
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,712
2nd row23,405
3rd row512,479
4th row96,858
5th row41,269
ValueCountFrequency (%)
1,102,3501
 
1.6%
66,7811
 
1.6%
44,7341
 
1.6%
232,5221
 
1.6%
26,1791
 
1.6%
239,7891
 
1.6%
59,4151
 
1.6%
75,5511
 
1.6%
35,2271
 
1.6%
40,9561
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:30.972472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,102,3501
 
1.6%
66,7811
 
1.6%
44,7341
 
1.6%
232,5221
 
1.6%
26,1791
 
1.6%
239,7891
 
1.6%
59,4151
 
1.6%
75,5511
 
1.6%
35,2271
 
1.6%
40,9561
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
143
11.0%
243
11.0%
542
10.7%
340
10.2%
437
9.4%
732
8.2%
928
7.1%
624
 
6.1%
820
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
143
13.1%
243
13.1%
542
12.8%
340
12.2%
437
11.3%
732
9.8%
928
8.5%
624
7.3%
820
6.1%
019
5.8%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
143
11.0%
243
11.0%
542
10.7%
340
10.2%
437
9.4%
732
8.2%
928
7.1%
624
 
6.1%
820
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
143
11.0%
243
11.0%
542
10.7%
340
10.2%
437
9.4%
732
8.2%
928
7.1%
624
 
6.1%
820
 
5.1%

Employed_2007
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
228,808
 
1
152,567
 
1
139,838
 
1
171,540
 
1
72,418
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row152,567
2nd row22,129
3rd row477,487
4th row92,549
5th row39,164
ValueCountFrequency (%)
228,8081
 
1.6%
152,5671
 
1.6%
139,8381
 
1.6%
171,5401
 
1.6%
72,4181
 
1.6%
37,5561
 
1.6%
30,1941
 
1.6%
3,0571
 
1.6%
45,5101
 
1.6%
63,7901
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:31.568046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
228,8081
 
1.6%
152,5671
 
1.6%
139,8381
 
1.6%
171,5401
 
1.6%
72,4181
 
1.6%
37,5561
 
1.6%
30,1941
 
1.6%
3,0571
 
1.6%
45,5101
 
1.6%
63,7901
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
541
10.5%
240
10.2%
336
9.2%
135
8.9%
435
8.9%
733
8.4%
032
8.2%
828
7.1%
926
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
541
12.5%
240
12.2%
336
11.0%
135
10.7%
435
10.7%
733
10.1%
032
9.8%
828
8.5%
926
7.9%
622
6.7%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
541
10.5%
240
10.2%
336
9.2%
135
8.9%
435
8.9%
733
8.4%
032
8.2%
828
7.1%
926
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
541
10.5%
240
10.2%
336
9.2%
135
8.9%
435
8.9%
733
8.4%
032
8.2%
828
7.1%
926
6.6%

Unemployed_2007
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
49,391
 
1
3,133
 
1
5,927
 
1
1,343
 
1
1,493
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.967741935
Min length3

Characters and Unicode

Total characters308
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,145
2nd row1,276
3rd row34,992
4th row4,309
5th row2,105
ValueCountFrequency (%)
49,3911
 
1.6%
3,1331
 
1.6%
5,9271
 
1.6%
1,3431
 
1.6%
1,4931
 
1.6%
2,9911
 
1.6%
25,5641
 
1.6%
2,8611
 
1.6%
4971
 
1.6%
9091
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:32.143666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
49,3911
 
1.6%
3,1331
 
1.6%
5,9271
 
1.6%
1,3431
 
1.6%
1,4931
 
1.6%
2,9911
 
1.6%
25,5641
 
1.6%
2,8611
 
1.6%
4971
 
1.6%
9091
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,56
18.2%
148
15.6%
230
9.7%
326
8.4%
926
8.4%
424
7.8%
523
7.5%
620
 
6.5%
720
 
6.5%
820
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
81.8%
Other Punctuation56
 
18.2%

Most frequent character per category

ValueCountFrequency (%)
148
19.0%
230
11.9%
326
10.3%
926
10.3%
424
9.5%
523
9.1%
620
7.9%
720
7.9%
820
7.9%
015
 
6.0%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.2%
148
15.6%
230
9.7%
326
8.4%
926
8.4%
424
7.8%
523
7.5%
620
 
6.5%
720
 
6.5%
820
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII308
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.2%
148
15.6%
230
9.7%
326
8.4%
926
8.4%
424
7.8%
523
7.5%
620
 
6.5%
720
 
6.5%
820
 
6.5%

Unemployment_rate_2007
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.70483871
Minimum3.4
Maximum6.8
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:32.356705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile3.7
Q14.3
median4.6
Q35.2
95-th percentile5.8
Maximum6.8
Range3.4
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6952832703
Coefficient of variation (CV)0.1477804688
Kurtosis0.03731049913
Mean4.70483871
Median Absolute Deviation (MAD)0.5
Skewness0.4288450382
Sum291.7
Variance0.483418826
MonotocityNot monotonic
2021-01-15T02:59:32.577482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4.36
 
9.7%
4.75
 
8.1%
4.55
 
8.1%
3.95
 
8.1%
5.84
 
6.5%
4.94
 
6.5%
4.44
 
6.5%
4.13
 
4.8%
5.43
 
4.8%
5.53
 
4.8%
Other values (12)20
32.3%
ValueCountFrequency (%)
3.42
 
3.2%
3.73
4.8%
3.95
8.1%
4.13
4.8%
4.22
 
3.2%
ValueCountFrequency (%)
6.81
 
1.6%
5.84
6.5%
5.71
 
1.6%
5.62
3.2%
5.53
4.8%

Civilian_labor_force_2008
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
56,222
 
1
1,106,224
 
1
45,922
 
1
931,595
 
1
50,282
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.338709677
Min length5

Characters and Unicode

Total characters393
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row160,552
2nd row24,123
3rd row523,746
4th row98,160
5th row41,766
ValueCountFrequency (%)
56,2221
 
1.6%
1,106,2241
 
1.6%
45,9221
 
1.6%
931,5951
 
1.6%
50,2821
 
1.6%
41,8021
 
1.6%
111,2931
 
1.6%
24,1231
 
1.6%
60,5251
 
1.6%
114,0181
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:33.158548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
56,2221
 
1.6%
1,106,2241
 
1.6%
45,9221
 
1.6%
931,5951
 
1.6%
50,2821
 
1.6%
41,8021
 
1.6%
111,2931
 
1.6%
24,1231
 
1.6%
60,5251
 
1.6%
114,0181
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
150
12.7%
249
12.5%
535
8.9%
334
8.7%
631
7.9%
930
7.6%
428
7.1%
024
 
6.1%
724
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number329
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
150
15.2%
249
14.9%
535
10.6%
334
10.3%
631
9.4%
930
9.1%
428
8.5%
024
7.3%
724
7.3%
824
7.3%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common393
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
150
12.7%
249
12.5%
535
8.9%
334
8.7%
631
7.9%
930
7.6%
428
7.1%
024
 
6.1%
724
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII393
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
150
12.7%
249
12.5%
535
8.9%
334
8.7%
631
7.9%
930
7.6%
428
7.1%
024
 
6.1%
724
 
6.1%

Employed_2008
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
148,768
 
1
20,062
 
1
472,088
 
1
22,938
 
1
484,145
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row152,756
2nd row22,578
3rd row484,145
4th row92,690
5th row39,222
ValueCountFrequency (%)
148,7681
 
1.6%
20,0621
 
1.6%
472,0881
 
1.6%
22,9381
 
1.6%
484,1451
 
1.6%
43,1321
 
1.6%
14,8221
 
1.6%
16,2371
 
1.6%
759,8991
 
1.6%
56,3141
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:33.741373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
148,7681
 
1.6%
20,0621
 
1.6%
472,0881
 
1.6%
22,9381
 
1.6%
484,1451
 
1.6%
43,1321
 
1.6%
14,8221
 
1.6%
16,2371
 
1.6%
759,8991
 
1.6%
56,3141
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
341
10.5%
140
10.2%
240
10.2%
935
8.9%
733
8.4%
433
8.4%
530
7.7%
626
6.6%
825
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
341
12.5%
140
12.2%
240
12.2%
935
10.7%
733
10.1%
433
10.1%
530
9.1%
626
7.9%
825
7.6%
025
7.6%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
341
10.5%
140
10.2%
240
10.2%
935
8.9%
733
8.4%
433
8.4%
530
7.7%
626
6.6%
825
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
341
10.5%
140
10.2%
240
10.2%
935
8.9%
733
8.4%
433
8.4%
530
7.7%
626
6.6%
825
 
6.4%

Unemployed_2008
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,545
 
2
1,432
 
1
66,645
 
1
2,484
 
1
3,918
 
1
Other values (56)
56 

Length

Max length6
Median length5
Mean length5.016129032
Min length3

Characters and Unicode

Total characters311
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)96.8%

Sample

1st row7,796
2nd row1,545
3rd row39,601
4th row5,470
5th row2,544
ValueCountFrequency (%)
1,5452
 
3.2%
1,4321
 
1.6%
66,6451
 
1.6%
2,4841
 
1.6%
3,9181
 
1.6%
1,8031
 
1.6%
7,7961
 
1.6%
20,6751
 
1.6%
23,7431
 
1.6%
2,0651
 
1.6%
Other values (51)51
82.3%
2021-01-15T02:59:34.325632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,5452
 
3.2%
1,4321
 
1.6%
66,6451
 
1.6%
2,4841
 
1.6%
3,9181
 
1.6%
1,8031
 
1.6%
7,7961
 
1.6%
20,6751
 
1.6%
23,7431
 
1.6%
2,0651
 
1.6%
Other values (51)51
82.3%

Most occurring characters

ValueCountFrequency (%)
,57
18.3%
138
12.2%
238
12.2%
531
10.0%
329
9.3%
426
8.4%
921
 
6.8%
720
 
6.4%
618
 
5.8%
817
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number254
81.7%
Other Punctuation57
 
18.3%

Most frequent character per category

ValueCountFrequency (%)
138
15.0%
238
15.0%
531
12.2%
329
11.4%
426
10.2%
921
8.3%
720
7.9%
618
7.1%
817
6.7%
016
6.3%
ValueCountFrequency (%)
,57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common311
100.0%

Most frequent character per script

ValueCountFrequency (%)
,57
18.3%
138
12.2%
238
12.2%
531
10.0%
329
9.3%
426
8.4%
921
 
6.8%
720
 
6.4%
618
 
5.8%
817
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII311
100.0%

Most frequent character per block

ValueCountFrequency (%)
,57
18.3%
138
12.2%
238
12.2%
531
10.0%
329
9.3%
426
8.4%
921
 
6.8%
720
 
6.4%
618
 
5.8%
817
 
5.5%

Unemployment_rate_2008
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.769354839
Minimum4.1
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:34.548504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile4.705
Q15.2
median5.65
Q36.4
95-th percentile6.995
Maximum7.6
Range3.5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.7772563578
Coefficient of variation (CV)0.1347215381
Kurtosis-0.6462887806
Mean5.769354839
Median Absolute Deviation (MAD)0.55
Skewness0.2042897983
Sum357.7
Variance0.6041274458
MonotocityNot monotonic
2021-01-15T02:59:34.793802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.55
 
8.1%
6.74
 
6.5%
5.64
 
6.5%
4.93
 
4.8%
6.43
 
4.8%
5.73
 
4.8%
4.83
 
4.8%
5.93
 
4.8%
6.93
 
4.8%
5.23
 
4.8%
Other values (17)28
45.2%
ValueCountFrequency (%)
4.11
 
1.6%
4.41
 
1.6%
4.61
 
1.6%
4.71
 
1.6%
4.83
4.8%
ValueCountFrequency (%)
7.61
 
1.6%
7.12
3.2%
71
 
1.6%
6.93
4.8%
6.81
 
1.6%

Civilian_labor_force_2009
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
65,934
 
1
12,755
 
1
931,604
 
1
27,246
 
1
39,787
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.338709677
Min length5

Characters and Unicode

Total characters393
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,937
2nd row23,898
3rd row533,866
4th row97,500
5th row41,668
ValueCountFrequency (%)
65,9341
 
1.6%
12,7551
 
1.6%
931,6041
 
1.6%
27,2461
 
1.6%
39,7871
 
1.6%
32,1431
 
1.6%
692,9051
 
1.6%
89,9441
 
1.6%
246,0871
 
1.6%
19,3761
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:35.377378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65,9341
 
1.6%
12,7551
 
1.6%
931,6041
 
1.6%
27,2461
 
1.6%
39,7871
 
1.6%
32,1431
 
1.6%
692,9051
 
1.6%
89,9441
 
1.6%
246,0871
 
1.6%
19,3761
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
147
12.0%
344
11.2%
436
9.2%
635
8.9%
234
8.7%
030
7.6%
529
7.4%
926
6.6%
725
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number329
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
147
14.3%
344
13.4%
436
10.9%
635
10.6%
234
10.3%
030
9.1%
529
8.8%
926
7.9%
725
7.6%
823
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common393
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
147
12.0%
344
11.2%
436
9.2%
635
8.9%
234
8.7%
030
7.6%
529
7.4%
926
6.6%
725
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII393
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
147
12.0%
344
11.2%
436
9.2%
635
8.9%
234
8.7%
030
7.6%
529
7.4%
926
6.6%
725
 
6.4%

Employed_2009
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
29,121
 
1
12,341
 
1
19,147
 
1
33,175
 
1
24,124
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row148,183
2nd row21,816
3rd row469,624
4th row89,553
5th row38,079
ValueCountFrequency (%)
29,1211
 
1.6%
12,3411
 
1.6%
19,1471
 
1.6%
33,1751
 
1.6%
24,1241
 
1.6%
52,8261
 
1.6%
32,3091
 
1.6%
41,5731
 
1.6%
77,6801
 
1.6%
14,1981
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:37.019067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29,1211
 
1.6%
12,3411
 
1.6%
19,1471
 
1.6%
33,1751
 
1.6%
24,1241
 
1.6%
52,8261
 
1.6%
32,3091
 
1.6%
41,5731
 
1.6%
77,6801
 
1.6%
14,1981
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
151
13.0%
248
12.2%
342
10.7%
439
9.9%
626
6.6%
526
6.6%
026
6.6%
825
 
6.4%
724
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
151
15.5%
248
14.6%
342
12.8%
439
11.9%
626
7.9%
526
7.9%
026
7.9%
825
7.6%
724
7.3%
921
6.4%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
151
13.0%
248
12.2%
342
10.7%
439
9.9%
626
6.6%
526
6.6%
026
6.6%
825
 
6.4%
724
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
151
13.0%
248
12.2%
342
10.7%
439
9.9%
626
6.6%
526
6.6%
026
6.6%
825
 
6.4%
724
 
6.1%

Unemployed_2009
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
3,072
 
1
2,479
 
1
2,311
 
1
5,582
 
1
10,754
 
1
Other values (57)
57 

Length

Max length7
Median length5
Mean length5.177419355
Min length3

Characters and Unicode

Total characters321
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row10,754
2nd row2,082
3rd row64,242
4th row7,947
5th row3,589
ValueCountFrequency (%)
3,0721
 
1.6%
2,4791
 
1.6%
2,3111
 
1.6%
5,5821
 
1.6%
10,7541
 
1.6%
2261
 
1.6%
17,8391
 
1.6%
2,0031
 
1.6%
8391
 
1.6%
1,3831
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:37.591642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,0721
 
1.6%
2,4791
 
1.6%
2,3111
 
1.6%
5,5821
 
1.6%
10,7541
 
1.6%
2261
 
1.6%
17,8391
 
1.6%
2,0031
 
1.6%
8391
 
1.6%
1,3831
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,59
18.4%
142
13.1%
235
10.9%
430
9.3%
328
8.7%
724
7.5%
824
7.5%
022
 
6.9%
521
 
6.5%
920
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number262
81.6%
Other Punctuation59
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
142
16.0%
235
13.4%
430
11.5%
328
10.7%
724
9.2%
824
9.2%
022
8.4%
521
8.0%
920
7.6%
616
 
6.1%
ValueCountFrequency (%)
,59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common321
100.0%

Most frequent character per script

ValueCountFrequency (%)
,59
18.4%
142
13.1%
235
10.9%
430
9.3%
328
8.7%
724
7.5%
824
7.5%
022
 
6.9%
521
 
6.5%
920
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII321
100.0%

Most frequent character per block

ValueCountFrequency (%)
,59
18.4%
142
13.1%
235
10.9%
430
9.3%
328
8.7%
724
7.5%
824
7.5%
022
 
6.9%
521
 
6.5%
920
 
6.2%

Unemployment_rate_2009
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.217741935
Minimum5.9
Maximum12
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:37.831317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile6.705
Q17.5
median8.25
Q38.875
95-th percentile9.79
Maximum12
Range6.1
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation1.033143253
Coefficient of variation (CV)0.1257210632
Kurtosis1.79458331
Mean8.217741935
Median Absolute Deviation (MAD)0.65
Skewness0.6111829232
Sum509.5
Variance1.067384981
MonotocityNot monotonic
2021-01-15T02:59:38.081720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8.35
 
8.1%
7.44
 
6.5%
7.63
 
4.8%
7.33
 
4.8%
9.33
 
4.8%
8.43
 
4.8%
8.93
 
4.8%
8.13
 
4.8%
9.62
 
3.2%
9.82
 
3.2%
Other values (21)31
50.0%
ValueCountFrequency (%)
5.91
1.6%
6.31
1.6%
6.72
3.2%
6.81
1.6%
6.92
3.2%
ValueCountFrequency (%)
121
1.6%
9.91
1.6%
9.82
3.2%
9.62
3.2%
9.51
1.6%

Civilian_labor_force_2010
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
12,756
 
1
30,233
 
1
47,429
 
1
47,303
 
1
16,423
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row161,511
2nd row24,240
3rd row582,287
4th row97,844
5th row39,654
ValueCountFrequency (%)
12,7561
 
1.6%
30,2331
 
1.6%
47,4291
 
1.6%
47,3031
 
1.6%
16,4231
 
1.6%
22,1011
 
1.6%
54,7671
 
1.6%
39,7011
 
1.6%
112,2481
 
1.6%
15,9241
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:38.680935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12,7561
 
1.6%
30,2331
 
1.6%
47,4291
 
1.6%
47,3031
 
1.6%
16,4231
 
1.6%
22,1011
 
1.6%
54,7671
 
1.6%
39,7011
 
1.6%
112,2481
 
1.6%
15,9241
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
146
11.7%
240
10.2%
439
9.9%
334
8.7%
633
8.4%
533
8.4%
929
7.4%
826
6.6%
726
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
146
14.0%
240
12.2%
439
11.9%
334
10.4%
633
10.1%
533
10.1%
929
8.8%
826
7.9%
726
7.9%
022
6.7%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
146
11.7%
240
10.2%
439
9.9%
334
8.7%
633
8.4%
533
8.4%
929
7.4%
826
6.6%
726
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
146
11.7%
240
10.2%
439
9.9%
334
8.7%
633
8.4%
533
8.4%
929
7.4%
826
6.6%
726
6.6%

Employed_2010
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
103,684
 
1
2,256
 
1
20,249
 
1
24,448
 
1
72,974
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.306451613
Min length5

Characters and Unicode

Total characters391
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row150,264
2nd row22,022
3rd row512,477
4th row89,316
5th row35,881
ValueCountFrequency (%)
103,6841
 
1.6%
2,2561
 
1.6%
20,2491
 
1.6%
24,4481
 
1.6%
72,9741
 
1.6%
166,8301
 
1.6%
15,0781
 
1.6%
97,4671
 
1.6%
443,4761
 
1.6%
17,6161
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:39.263715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
103,6841
 
1.6%
2,2561
 
1.6%
20,2491
 
1.6%
24,4481
 
1.6%
72,9741
 
1.6%
166,8301
 
1.6%
15,0781
 
1.6%
97,4671
 
1.6%
443,4761
 
1.6%
17,6161
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
145
11.5%
245
11.5%
437
9.5%
035
9.0%
732
8.2%
832
8.2%
327
6.9%
626
6.6%
925
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
145
13.8%
245
13.8%
437
11.3%
035
10.7%
732
9.8%
832
9.8%
327
8.3%
626
8.0%
925
7.6%
523
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
145
11.5%
245
11.5%
437
9.5%
035
9.0%
732
8.2%
832
8.2%
327
6.9%
626
6.6%
925
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII391
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
145
11.5%
245
11.5%
437
9.5%
035
9.0%
732
8.2%
832
8.2%
327
6.9%
626
6.6%
925
 
6.4%

Unemployed_2010
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
2,768
 
1
822
 
1
2,182
 
1
10,380
 
1
4,523
 
1
Other values (57)
57 

Length

Max length7
Median length5
Mean length5.177419355
Min length3

Characters and Unicode

Total characters321
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row11,247
2nd row2,218
3rd row69,810
4th row8,528
5th row3,773
ValueCountFrequency (%)
2,7681
 
1.6%
8221
 
1.6%
2,1821
 
1.6%
10,3801
 
1.6%
4,5231
 
1.6%
3,0721
 
1.6%
20,4601
 
1.6%
1,8351
 
1.6%
1,4201
 
1.6%
1,9661
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:39.836283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,7681
 
1.6%
8221
 
1.6%
2,1821
 
1.6%
10,3801
 
1.6%
4,5231
 
1.6%
3,0721
 
1.6%
20,4601
 
1.6%
1,8351
 
1.6%
1,4201
 
1.6%
1,9661
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,59
18.4%
143
13.4%
243
13.4%
430
9.3%
524
7.5%
623
 
7.2%
023
 
7.2%
322
 
6.9%
820
 
6.2%
718
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number262
81.6%
Other Punctuation59
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
143
16.4%
243
16.4%
430
11.5%
524
9.2%
623
8.8%
023
8.8%
322
8.4%
820
7.6%
718
6.9%
916
 
6.1%
ValueCountFrequency (%)
,59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common321
100.0%

Most frequent character per script

ValueCountFrequency (%)
,59
18.4%
143
13.4%
243
13.4%
430
9.3%
524
7.5%
623
 
7.2%
023
 
7.2%
322
 
6.9%
820
 
6.2%
718
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII321
100.0%

Most frequent character per block

ValueCountFrequency (%)
,59
18.4%
143
13.4%
243
13.4%
430
9.3%
524
7.5%
623
 
7.2%
023
 
7.2%
322
 
6.9%
820
 
6.2%
718
 
5.6%

Unemployment_rate_2010
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.60483871
Minimum6.2
Maximum12
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:40.077787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile7.105
Q17.8
median8.6
Q39.2
95-th percentile10.29
Maximum12
Range5.8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.062077676
Coefficient of variation (CV)0.1234279586
Kurtosis0.6070844798
Mean8.60483871
Median Absolute Deviation (MAD)0.75
Skewness0.4286063846
Sum533.5
Variance1.12800899
MonotocityNot monotonic
2021-01-15T02:59:40.326395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8.95
 
8.1%
85
 
8.1%
9.63
 
4.8%
7.63
 
4.8%
9.23
 
4.8%
9.13
 
4.8%
8.63
 
4.8%
7.43
 
4.8%
9.82
 
3.2%
7.82
 
3.2%
Other values (24)30
48.4%
ValueCountFrequency (%)
6.21
1.6%
6.91
1.6%
71
1.6%
7.11
1.6%
7.21
1.6%
ValueCountFrequency (%)
121
1.6%
10.62
3.2%
10.31
1.6%
10.11
1.6%
9.91
1.6%

Civilian_labor_force_2011
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
23,657
 
1
146,309
 
1
770,533
 
1
18,881
 
1
882,224
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,289
2nd row23,657
3rd row584,353
4th row94,727
5th row38,540
ValueCountFrequency (%)
23,6571
 
1.6%
146,3091
 
1.6%
770,5331
 
1.6%
18,8811
 
1.6%
882,2241
 
1.6%
38,5401
 
1.6%
57,9941
 
1.6%
115,6691
 
1.6%
232,6221
 
1.6%
55,7561
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:40.916670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23,6571
 
1.6%
146,3091
 
1.6%
770,5331
 
1.6%
18,8811
 
1.6%
882,2241
 
1.6%
38,5401
 
1.6%
57,9941
 
1.6%
115,6691
 
1.6%
232,6221
 
1.6%
55,7561
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
243
11.0%
142
10.7%
340
10.2%
837
9.4%
731
7.9%
529
7.4%
429
7.4%
029
7.4%
627
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
243
13.1%
142
12.8%
340
12.2%
837
11.3%
731
9.5%
529
8.8%
429
8.8%
029
8.8%
627
8.2%
921
6.4%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
243
11.0%
142
10.7%
340
10.2%
837
9.4%
731
7.9%
529
7.4%
429
7.4%
029
7.4%
627
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
243
11.0%
142
10.7%
340
10.2%
837
9.4%
731
7.9%
529
7.4%
429
7.4%
029
7.4%
627
6.9%

Employed_2011
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
17,232
 
1
636,681
 
1
22,284
 
1
51,752
 
1
14,163
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.306451613
Min length5

Characters and Unicode

Total characters391
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row147,330
2nd row21,545
3rd row514,635
4th row86,579
5th row34,943
ValueCountFrequency (%)
17,2321
 
1.6%
636,6811
 
1.6%
22,2841
 
1.6%
51,7521
 
1.6%
14,1631
 
1.6%
107,9731
 
1.6%
21,0341
 
1.6%
514,6351
 
1.6%
813,2751
 
1.6%
23,8321
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:41.489865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17,2321
 
1.6%
636,6811
 
1.6%
22,2841
 
1.6%
51,7521
 
1.6%
14,1631
 
1.6%
107,9731
 
1.6%
21,0341
 
1.6%
514,6351
 
1.6%
813,2751
 
1.6%
23,8321
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
155
14.1%
243
11.0%
335
9.0%
433
8.4%
533
8.4%
929
7.4%
026
6.6%
825
 
6.4%
724
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
155
16.8%
243
13.1%
335
10.7%
433
10.1%
533
10.1%
929
8.9%
026
8.0%
825
7.6%
724
7.3%
624
7.3%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
155
14.1%
243
11.0%
335
9.0%
433
8.4%
533
8.4%
929
7.4%
026
6.6%
825
 
6.4%
724
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII391
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
155
14.1%
243
11.0%
335
9.0%
433
8.4%
533
8.4%
929
7.4%
026
6.6%
825
 
6.4%
724
 
6.1%

Unemployed_2011
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
2,837
 
1
89,671
 
1
3,197
 
1
111,959
 
1
1,649
 
1
Other values (57)
57 

Length

Max length7
Median length5
Mean length5.161290323
Min length3

Characters and Unicode

Total characters320
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row10,959
2nd row2,112
3rd row69,718
4th row8,148
5th row3,597
ValueCountFrequency (%)
2,8371
 
1.6%
89,6711
 
1.6%
3,1971
 
1.6%
111,9591
 
1.6%
1,6491
 
1.6%
3,6841
 
1.6%
2,0241
 
1.6%
5,0761
 
1.6%
19,8501
 
1.6%
2,1121
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:42.051911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,8371
 
1.6%
89,6711
 
1.6%
3,1971
 
1.6%
111,9591
 
1.6%
1,6491
 
1.6%
3,6841
 
1.6%
2,0241
 
1.6%
5,0761
 
1.6%
19,8501
 
1.6%
2,1121
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,59
18.4%
133
10.3%
631
9.7%
229
9.1%
928
8.8%
827
8.4%
725
7.8%
424
7.5%
022
 
6.9%
322
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number261
81.6%
Other Punctuation59
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
133
12.6%
631
11.9%
229
11.1%
928
10.7%
827
10.3%
725
9.6%
424
9.2%
022
8.4%
322
8.4%
520
7.7%
ValueCountFrequency (%)
,59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common320
100.0%

Most frequent character per script

ValueCountFrequency (%)
,59
18.4%
133
10.3%
631
9.7%
229
9.1%
928
8.8%
827
8.4%
725
7.8%
424
7.5%
022
 
6.9%
322
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII320
100.0%

Most frequent character per block

ValueCountFrequency (%)
,59
18.4%
133
10.3%
631
9.7%
229
9.1%
928
8.8%
827
8.4%
725
7.8%
424
7.5%
022
 
6.9%
322
 
6.9%

Unemployment_rate_2011
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.424193548
Minimum6.1
Maximum11.9
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:42.303510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile6.905
Q17.7
median8.2
Q39.1
95-th percentile10.39
Maximum11.9
Range5.8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.119380022
Coefficient of variation (CV)0.1328768167
Kurtosis0.4883150493
Mean8.424193548
Median Absolute Deviation (MAD)0.7
Skewness0.5986166629
Sum522.3
Variance1.253011634
MonotocityNot monotonic
2021-01-15T02:59:42.543471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7.75
 
8.1%
9.14
 
6.5%
7.84
 
6.5%
8.74
 
6.5%
7.14
 
6.5%
7.63
 
4.8%
7.53
 
4.8%
8.13
 
4.8%
8.93
 
4.8%
9.62
 
3.2%
Other values (24)27
43.5%
ValueCountFrequency (%)
6.11
1.6%
6.71
1.6%
6.81
1.6%
6.91
1.6%
71
1.6%
ValueCountFrequency (%)
11.91
1.6%
10.81
1.6%
10.61
1.6%
10.41
1.6%
10.21
1.6%

Civilian_labor_force_2012
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,124,169
 
1
18,873
 
1
894,987
 
1
34,620
 
1
21,589
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row160,657
2nd row23,691
3rd row594,943
4th row94,012
5th row38,308
ValueCountFrequency (%)
1,124,1691
 
1.6%
18,8731
 
1.6%
894,9871
 
1.6%
34,6201
 
1.6%
21,5891
 
1.6%
78,0521
 
1.6%
18,1761
 
1.6%
21,7961
 
1.6%
30,5381
 
1.6%
56,0461
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:43.125454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,124,1691
 
1.6%
18,8731
 
1.6%
894,9871
 
1.6%
34,6201
 
1.6%
21,5891
 
1.6%
78,0521
 
1.6%
18,1761
 
1.6%
21,7961
 
1.6%
30,5381
 
1.6%
56,0461
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
145
11.5%
240
10.2%
435
8.9%
533
8.4%
333
8.4%
630
7.7%
930
7.7%
828
7.1%
027
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
145
13.7%
240
12.2%
435
10.7%
533
10.1%
333
10.1%
630
9.1%
930
9.1%
828
8.5%
027
8.2%
727
8.2%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
145
11.5%
240
10.2%
435
8.9%
533
8.4%
333
8.4%
630
7.7%
930
7.7%
828
7.1%
027
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
145
11.5%
240
10.2%
435
8.9%
533
8.4%
333
8.4%
630
7.7%
930
7.7%
828
7.1%
027
6.9%

Employed_2012
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
14,946
 
1
2,187
 
1
72,058
 
1
85,835
 
1
31,675
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row149,260
2nd row21,697
3rd row520,987
4th row85,835
5th row34,733
ValueCountFrequency (%)
14,9461
 
1.6%
2,1871
 
1.6%
72,0581
 
1.6%
85,8351
 
1.6%
31,6751
 
1.6%
56,0551
 
1.6%
1,031,0211
 
1.6%
29,6251
 
1.6%
27,8611
 
1.6%
197,5831
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:43.709627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14,9461
 
1.6%
2,1871
 
1.6%
72,0581
 
1.6%
85,8351
 
1.6%
31,6751
 
1.6%
56,0551
 
1.6%
1,031,0211
 
1.6%
29,6251
 
1.6%
27,8611
 
1.6%
197,5831
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
447
12.1%
145
11.5%
241
10.5%
334
8.7%
530
7.7%
627
6.9%
727
6.9%
827
6.9%
926
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
447
14.4%
145
13.8%
241
12.6%
334
10.4%
530
9.2%
627
8.3%
727
8.3%
827
8.3%
926
8.0%
022
6.7%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
447
12.1%
145
11.5%
241
10.5%
334
8.7%
530
7.7%
627
6.9%
727
6.9%
827
6.9%
926
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
447
12.1%
145
11.5%
241
10.5%
334
8.7%
530
7.7%
627
6.9%
727
6.9%
827
6.9%
926
6.7%

Unemployed_2012
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,887
 
1
2,945
 
1
2,230
 
1
8,005
 
1
2,677
 
1
Other values (57)
57 

Length

Max length7
Median length5
Mean length5.161290323
Min length3

Characters and Unicode

Total characters320
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row11,397
2nd row1,994
3rd row73,956
4th row8,177
5th row3,575
ValueCountFrequency (%)
1,8871
 
1.6%
2,9451
 
1.6%
2,2301
 
1.6%
8,0051
 
1.6%
2,6771
 
1.6%
2,4401
 
1.6%
3,1011
 
1.6%
116,3631
 
1.6%
9431
 
1.6%
1,2961
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:44.279980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,8871
 
1.6%
2,9451
 
1.6%
2,2301
 
1.6%
8,0051
 
1.6%
2,6771
 
1.6%
2,4401
 
1.6%
3,1011
 
1.6%
116,3631
 
1.6%
9431
 
1.6%
1,2961
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,59
18.4%
237
11.6%
133
10.3%
431
9.7%
330
9.4%
730
9.4%
923
 
7.2%
620
 
6.2%
820
 
6.2%
519
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number261
81.6%
Other Punctuation59
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
237
14.2%
133
12.6%
431
11.9%
330
11.5%
730
11.5%
923
8.8%
620
7.7%
820
7.7%
519
7.3%
018
6.9%
ValueCountFrequency (%)
,59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common320
100.0%

Most frequent character per script

ValueCountFrequency (%)
,59
18.4%
237
11.6%
133
10.3%
431
9.7%
330
9.4%
730
9.4%
923
 
7.2%
620
 
6.2%
820
 
6.2%
519
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII320
100.0%

Most frequent character per block

ValueCountFrequency (%)
,59
18.4%
237
11.6%
133
10.3%
431
9.7%
330
9.4%
730
9.4%
923
 
7.2%
620
 
6.2%
820
 
6.2%
519
 
5.9%

Unemployment_rate_2012
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.635483871
Minimum6.1
Maximum12.4
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:44.524490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile7.005
Q17.9
median8.45
Q39.375
95-th percentile10.685
Maximum12.4
Range6.3
Interquartile range (IQR)1.475

Descriptive statistics

Standard deviation1.167889616
Coefficient of variation (CV)0.1352431009
Kurtosis0.7816551151
Mean8.635483871
Median Absolute Deviation (MAD)0.75
Skewness0.6364600957
Sum535.4
Variance1.363966155
MonotocityNot monotonic
2021-01-15T02:59:44.774509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.96
 
9.7%
8.35
 
8.1%
7.84
 
6.5%
8.63
 
4.8%
9.33
 
4.8%
7.33
 
4.8%
82
 
3.2%
8.72
 
3.2%
7.72
 
3.2%
9.42
 
3.2%
Other values (23)30
48.4%
ValueCountFrequency (%)
6.11
1.6%
6.81
1.6%
72
3.2%
7.11
1.6%
7.21
1.6%
ValueCountFrequency (%)
12.41
1.6%
112
3.2%
10.71
1.6%
10.41
1.6%
10.21
1.6%

Civilian_labor_force_2013
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
21,544
 
1
91,934
 
1
38,100
 
1
90,350
 
1
12,245
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row160,805
2nd row23,799
3rd row603,461
4th row91,934
5th row38,100
ValueCountFrequency (%)
21,5441
 
1.6%
91,9341
 
1.6%
38,1001
 
1.6%
90,3501
 
1.6%
12,2451
 
1.6%
30,5341
 
1.6%
150,7771
 
1.6%
160,8051
 
1.6%
697,0791
 
1.6%
45,6871
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:45.379579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,5441
 
1.6%
91,9341
 
1.6%
38,1001
 
1.6%
90,3501
 
1.6%
12,2451
 
1.6%
30,5341
 
1.6%
150,7771
 
1.6%
160,8051
 
1.6%
697,0791
 
1.6%
45,6871
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
154
13.8%
036
9.2%
335
8.9%
435
8.9%
833
8.4%
733
8.4%
231
7.9%
529
7.4%
925
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
154
16.5%
036
11.0%
335
10.7%
435
10.7%
833
10.1%
733
10.1%
231
9.5%
529
8.8%
925
7.6%
617
 
5.2%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
154
13.8%
036
9.2%
335
8.9%
435
8.9%
833
8.4%
733
8.4%
231
7.9%
529
7.4%
925
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
154
13.8%
036
9.2%
335
8.9%
435
8.9%
833
8.4%
733
8.4%
231
7.9%
529
7.4%
925
 
6.4%

Employed_2013
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
201,233
 
1
27,656
 
1
22,426
 
1
19,394
 
1
1,098,648
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,059
2nd row22,014
3rd row532,428
4th row84,779
5th row34,851
ValueCountFrequency (%)
201,2331
 
1.6%
27,6561
 
1.6%
22,4261
 
1.6%
19,3941
 
1.6%
1,098,6481
 
1.6%
29,6401
 
1.6%
96,1581
 
1.6%
730,0421
 
1.6%
14,0571
 
1.6%
29,9361
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:45.944624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201,2331
 
1.6%
27,6561
 
1.6%
22,4261
 
1.6%
19,3941
 
1.6%
1,098,6481
 
1.6%
29,6401
 
1.6%
96,1581
 
1.6%
730,0421
 
1.6%
14,0571
 
1.6%
29,9361
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
145
11.5%
241
10.5%
437
9.5%
836
9.2%
335
9.0%
934
8.7%
526
6.7%
726
6.7%
024
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
145
13.8%
241
12.6%
437
11.3%
836
11.0%
335
10.7%
934
10.4%
526
8.0%
726
8.0%
024
7.4%
622
6.7%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
145
11.5%
241
10.5%
437
9.5%
836
9.2%
335
9.0%
934
8.7%
526
6.7%
726
6.7%
024
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
145
11.5%
241
10.5%
437
9.5%
836
9.2%
335
9.0%
934
8.7%
526
6.7%
726
6.7%
024
 
6.2%

Unemployed_2013
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,158
 
1
15,849
 
1
34,122
 
1
88,426
 
1
6,865
 
1
Other values (57)
57 

Length

Max length7
Median length5
Mean length5.112903226
Min length3

Characters and Unicode

Total characters317
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row9,746
2nd row1,785
3rd row71,033
4th row7,155
5th row3,249
ValueCountFrequency (%)
1,1581
 
1.6%
15,8491
 
1.6%
34,1221
 
1.6%
88,4261
 
1.6%
6,8651
 
1.6%
2,0871
 
1.6%
9,7461
 
1.6%
26,1331
 
1.6%
7,1551
 
1.6%
2,2401
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:46.518548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,1581
 
1.6%
15,8491
 
1.6%
34,1221
 
1.6%
88,4261
 
1.6%
6,8651
 
1.6%
2,0871
 
1.6%
9,7461
 
1.6%
26,1331
 
1.6%
7,1551
 
1.6%
2,2401
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,59
18.6%
151
16.1%
235
11.0%
828
8.8%
326
8.2%
424
7.6%
522
 
6.9%
921
 
6.6%
719
 
6.0%
617
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number258
81.4%
Other Punctuation59
 
18.6%

Most frequent character per category

ValueCountFrequency (%)
151
19.8%
235
13.6%
828
10.9%
326
10.1%
424
9.3%
522
8.5%
921
8.1%
719
 
7.4%
617
 
6.6%
015
 
5.8%
ValueCountFrequency (%)
,59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common317
100.0%

Most frequent character per script

ValueCountFrequency (%)
,59
18.6%
151
16.1%
235
11.0%
828
8.8%
326
8.2%
424
7.6%
522
 
6.9%
921
 
6.6%
719
 
6.0%
617
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII317
100.0%

Most frequent character per block

ValueCountFrequency (%)
,59
18.6%
151
16.1%
235
11.0%
828
8.8%
326
8.2%
424
7.6%
522
 
6.9%
921
 
6.6%
719
 
6.0%
617
 
5.4%

Unemployment_rate_2013
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.70483871
Minimum5.2
Maximum11.8
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:46.756433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile6.005
Q16.8
median7.6
Q38.475
95-th percentile9.5
Maximum11.8
Range6.6
Interquartile range (IQR)1.675

Descriptive statistics

Standard deviation1.210937507
Coefficient of variation (CV)0.1571658477
Kurtosis0.848023021
Mean7.70483871
Median Absolute Deviation (MAD)0.8
Skewness0.5894014697
Sum477.7
Variance1.466369646
MonotocityNot monotonic
2021-01-15T02:59:47.007388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6.85
 
8.1%
7.53
 
4.8%
8.83
 
4.8%
6.73
 
4.8%
7.43
 
4.8%
9.53
 
4.8%
7.93
 
4.8%
6.33
 
4.8%
8.52
 
3.2%
72
 
3.2%
Other values (23)32
51.6%
ValueCountFrequency (%)
5.21
1.6%
5.81
1.6%
5.91
1.6%
61
1.6%
6.12
3.2%
ValueCountFrequency (%)
11.81
 
1.6%
9.91
 
1.6%
9.53
4.8%
9.41
 
1.6%
9.22
3.2%

Civilian_labor_force_2014
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
8,603
 
1
33,269
 
1
76,083
 
1
470,532
 
1
47,068
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row157,231
2nd row23,317
3rd row603,659
4th row87,974
5th row36,618
ValueCountFrequency (%)
8,6031
 
1.6%
33,2691
 
1.6%
76,0831
 
1.6%
470,5321
 
1.6%
47,0681
 
1.6%
54,7461
 
1.6%
20,7111
 
1.6%
11,8641
 
1.6%
18,0421
 
1.6%
224,6091
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:47.621595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,6031
 
1.6%
33,2691
 
1.6%
76,0831
 
1.6%
470,5321
 
1.6%
47,0681
 
1.6%
54,7461
 
1.6%
20,7111
 
1.6%
11,8641
 
1.6%
18,0421
 
1.6%
224,6091
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
241
10.5%
140
10.2%
336
9.2%
535
8.9%
435
8.9%
631
7.9%
031
7.9%
730
7.7%
826
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
241
12.5%
140
12.2%
336
11.0%
535
10.7%
435
10.7%
631
9.5%
031
9.5%
730
9.1%
826
7.9%
923
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
241
10.5%
140
10.2%
336
9.2%
535
8.9%
435
8.9%
631
7.9%
031
7.9%
730
7.7%
826
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
241
10.5%
140
10.2%
336
9.2%
535
8.9%
435
8.9%
631
7.9%
031
7.9%
730
7.7%
826
6.6%

Employed_2014
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
20,597
 
1
7,999
 
1
11,028
 
1
203,323
 
1
14,639
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row149,537
2nd row21,866
3rd row544,535
4th row82,128
5th row34,052
ValueCountFrequency (%)
20,5971
 
1.6%
7,9991
 
1.6%
11,0281
 
1.6%
203,3231
 
1.6%
14,6391
 
1.6%
34,0521
 
1.6%
41,6551
 
1.6%
1,121,0721
 
1.6%
82,9581
 
1.6%
420,9781
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:48.196783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20,5971
 
1.6%
7,9991
 
1.6%
11,0281
 
1.6%
203,3231
 
1.6%
14,6391
 
1.6%
34,0521
 
1.6%
41,6551
 
1.6%
1,121,0721
 
1.6%
82,9581
 
1.6%
420,9781
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
247
12.1%
145
11.5%
341
10.5%
534
8.7%
432
8.2%
731
7.9%
926
6.7%
826
6.7%
625
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
247
14.4%
145
13.8%
341
12.6%
534
10.4%
432
9.8%
731
9.5%
926
8.0%
826
8.0%
625
7.7%
019
5.8%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
247
12.1%
145
11.5%
341
10.5%
534
8.7%
432
8.2%
731
7.9%
926
6.7%
826
6.7%
625
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
247
12.1%
145
11.5%
341
10.5%
534
8.7%
432
8.2%
731
7.9%
926
6.7%
826
6.7%
625
 
6.4%

Unemployed_2014
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
2,187
 
1
988
 
1
1,713
 
1
21,152
 
1
2,146
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.983870968
Min length3

Characters and Unicode

Total characters309
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row7,694
2nd row1,451
3rd row59,124
4th row5,846
5th row2,566
ValueCountFrequency (%)
2,1871
 
1.6%
9881
 
1.6%
1,7131
 
1.6%
21,1521
 
1.6%
2,1461
 
1.6%
9,6431
 
1.6%
1,4001
 
1.6%
7,5691
 
1.6%
12,5041
 
1.6%
6041
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:48.750234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,1871
 
1.6%
9881
 
1.6%
1,7131
 
1.6%
21,1521
 
1.6%
2,1461
 
1.6%
9,6431
 
1.6%
1,4001
 
1.6%
7,5691
 
1.6%
12,5041
 
1.6%
6041
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,56
18.1%
138
12.3%
434
11.0%
528
9.1%
228
9.1%
325
8.1%
622
 
7.1%
721
 
6.8%
921
 
6.8%
019
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
81.9%
Other Punctuation56
 
18.1%

Most frequent character per category

ValueCountFrequency (%)
138
15.0%
434
13.4%
528
11.1%
228
11.1%
325
9.9%
622
8.7%
721
8.3%
921
8.3%
019
7.5%
817
6.7%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common309
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.1%
138
12.3%
434
11.0%
528
9.1%
228
9.1%
325
8.1%
622
 
7.1%
721
 
6.8%
921
 
6.8%
019
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII309
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.1%
138
12.3%
434
11.0%
528
9.1%
228
9.1%
325
8.1%
622
 
7.1%
721
 
6.8%
921
 
6.8%
019
 
6.1%

Unemployment_rate_2014
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.327419355
Minimum4.4
Maximum9.8
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:48.972111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile4.805
Q15.6
median6.25
Q36.9
95-th percentile7.795
Maximum9.8
Range5.4
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.013380862
Coefficient of variation (CV)0.160157057
Kurtosis0.9292265097
Mean6.327419355
Median Absolute Deviation (MAD)0.65
Skewness0.5628300585
Sum392.3
Variance1.026940772
MonotocityNot monotonic
2021-01-15T02:59:49.240664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6.15
 
8.1%
6.65
 
8.1%
6.23
 
4.8%
5.43
 
4.8%
5.73
 
4.8%
6.83
 
4.8%
73
 
4.8%
5.82
 
3.2%
6.92
 
3.2%
6.52
 
3.2%
Other values (22)31
50.0%
ValueCountFrequency (%)
4.41
1.6%
4.72
3.2%
4.81
1.6%
4.92
3.2%
5.11
1.6%
ValueCountFrequency (%)
9.81
1.6%
8.11
1.6%
7.91
1.6%
7.81
1.6%
7.72
3.2%

Civilian_labor_force_2015
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
88,339
 
1
158,470
 
1
43,710
 
1
28,703
 
1
81,577
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,470
2nd row20,341
3rd row605,128
4th row86,781
5th row35,212
ValueCountFrequency (%)
88,3391
 
1.6%
158,4701
 
1.6%
43,7101
 
1.6%
28,7031
 
1.6%
81,5771
 
1.6%
37,0661
 
1.6%
18,3771
 
1.6%
23,0861
 
1.6%
31,2461
 
1.6%
23,3841
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:49.823982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
88,3391
 
1.6%
158,4701
 
1.6%
43,7101
 
1.6%
28,7031
 
1.6%
81,5771
 
1.6%
37,0661
 
1.6%
18,3771
 
1.6%
23,0861
 
1.6%
31,2461
 
1.6%
23,3841
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
147
12.0%
742
10.7%
242
10.7%
437
9.4%
336
9.2%
833
8.4%
527
6.9%
025
 
6.4%
622
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
147
14.3%
742
12.8%
242
12.8%
437
11.3%
336
11.0%
833
10.1%
527
8.2%
025
7.6%
622
6.7%
917
 
5.2%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
147
12.0%
742
10.7%
242
10.7%
437
9.4%
336
9.2%
833
8.4%
527
6.9%
025
 
6.4%
622
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
147
12.0%
742
10.7%
242
10.7%
437
9.4%
336
9.2%
833
8.4%
527
6.9%
025
 
6.4%
622
 
5.6%

Employed_2015
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
27,215
 
1
206,436
 
1
864,614
 
1
29,605
 
1
7,800
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,584
2nd row19,019
3rd row558,075
4th row81,599
5th row32,998
ValueCountFrequency (%)
27,2151
 
1.6%
206,4361
 
1.6%
864,6141
 
1.6%
29,6051
 
1.6%
7,8001
 
1.6%
32,9981
 
1.6%
18,3281
 
1.6%
169,6701
 
1.6%
1,097,7061
 
1.6%
81,5991
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:50.414661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27,2151
 
1.6%
206,4361
 
1.6%
864,6141
 
1.6%
29,6051
 
1.6%
7,8001
 
1.6%
32,9981
 
1.6%
18,3281
 
1.6%
169,6701
 
1.6%
1,097,7061
 
1.6%
81,5991
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
154
13.8%
236
9.2%
834
8.7%
432
8.2%
031
7.9%
631
7.9%
730
7.7%
529
7.4%
926
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
154
16.6%
236
11.0%
834
10.4%
432
9.8%
031
9.5%
631
9.5%
730
9.2%
529
8.9%
926
8.0%
323
7.1%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
154
13.8%
236
9.2%
834
8.7%
432
8.2%
031
7.9%
631
7.9%
730
7.7%
529
7.4%
926
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
154
13.8%
236
9.2%
834
8.7%
432
8.2%
031
7.9%
631
7.9%
730
7.7%
529
7.4%
926
6.7%

Unemployed_2015
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
3,064
 
1
21,709
 
1
2,157
 
1
2,090
 
1
5,443
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.983870968
Min length3

Characters and Unicode

Total characters309
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,886
2nd row1,322
3rd row47,053
4th row5,182
5th row2,214
ValueCountFrequency (%)
3,0641
 
1.6%
21,7091
 
1.6%
2,1571
 
1.6%
2,0901
 
1.6%
5,4431
 
1.6%
1,7711
 
1.6%
2,7531
 
1.6%
2,2161
 
1.6%
1,9781
 
1.6%
44,2351
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:50.967135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,0641
 
1.6%
21,7091
 
1.6%
2,1571
 
1.6%
2,0901
 
1.6%
5,4431
 
1.6%
1,7711
 
1.6%
2,7531
 
1.6%
2,2161
 
1.6%
1,9781
 
1.6%
44,2351
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,56
18.1%
148
15.5%
231
10.0%
825
8.1%
624
7.8%
424
7.8%
322
 
7.1%
522
 
7.1%
920
 
6.5%
719
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253
81.9%
Other Punctuation56
 
18.1%

Most frequent character per category

ValueCountFrequency (%)
148
19.0%
231
12.3%
825
9.9%
624
9.5%
424
9.5%
322
8.7%
522
8.7%
920
7.9%
719
 
7.5%
018
 
7.1%
ValueCountFrequency (%)
,56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common309
100.0%

Most frequent character per script

ValueCountFrequency (%)
,56
18.1%
148
15.5%
231
10.0%
825
8.1%
624
7.8%
424
7.8%
322
 
7.1%
522
 
7.1%
920
 
6.5%
719
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII309
100.0%

Most frequent character per block

ValueCountFrequency (%)
,56
18.1%
148
15.5%
231
10.0%
825
8.1%
624
7.8%
424
7.8%
322
 
7.1%
522
 
7.1%
920
 
6.5%
719
 
6.1%

Unemployment_rate_2015
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size624.0 B
5.0
24 
6.0
22 
7.0
4.0
8.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row4.0
2nd row7.0
3rd row8.0
4th row6.0
5th row6.0
ValueCountFrequency (%)
5.024
38.7%
6.022
35.5%
7.09
 
14.5%
4.06
 
9.7%
8.01
 
1.6%
2021-01-15T02:59:51.455720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T02:59:51.605073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
5.024
38.7%
6.022
35.5%
7.09
 
14.5%
4.06
 
9.7%
8.01
 
1.6%

Most occurring characters

ValueCountFrequency (%)
.62
33.3%
062
33.3%
524
 
12.9%
622
 
11.8%
79
 
4.8%
46
 
3.2%
81
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number124
66.7%
Other Punctuation62
33.3%

Most frequent character per category

ValueCountFrequency (%)
062
50.0%
524
 
19.4%
622
 
17.7%
79
 
7.3%
46
 
4.8%
81
 
0.8%
ValueCountFrequency (%)
.62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common186
100.0%

Most frequent character per script

ValueCountFrequency (%)
.62
33.3%
062
33.3%
524
 
12.9%
622
 
11.8%
79
 
4.8%
46
 
3.2%
81
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII186
100.0%

Most frequent character per block

ValueCountFrequency (%)
.62
33.3%
062
33.3%
524
 
12.9%
622
 
11.8%
79
 
4.8%
46
 
3.2%
81
 
0.5%

Civilian_labor_force_2016
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
50,674
 
1
18,102
 
1
31,460
 
1
11,739
 
1
55,054
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.322580645
Min length5

Characters and Unicode

Total characters392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,435
2nd row20,052
3rd row607,295
4th row85,851
5th row34,849
ValueCountFrequency (%)
50,6741
 
1.6%
18,1021
 
1.6%
31,4601
 
1.6%
11,7391
 
1.6%
55,0541
 
1.6%
43,1521
 
1.6%
220,8231
 
1.6%
23,0101
 
1.6%
152,4791
 
1.6%
2,3631
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:52.150424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50,6741
 
1.6%
18,1021
 
1.6%
31,4601
 
1.6%
11,7391
 
1.6%
55,0541
 
1.6%
43,1521
 
1.6%
220,8231
 
1.6%
23,0101
 
1.6%
152,4791
 
1.6%
2,3631
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.3%
153
13.5%
243
11.0%
338
9.7%
532
8.2%
431
7.9%
028
7.1%
928
7.1%
827
6.9%
725
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number328
83.7%
Other Punctuation64
 
16.3%

Most frequent character per category

ValueCountFrequency (%)
153
16.2%
243
13.1%
338
11.6%
532
9.8%
431
9.5%
028
8.5%
928
8.5%
827
8.2%
725
7.6%
623
7.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.3%
153
13.5%
243
11.0%
338
9.7%
532
8.2%
431
7.9%
028
7.1%
928
7.1%
827
6.9%
725
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.3%
153
13.5%
243
11.0%
338
9.7%
532
8.2%
431
7.9%
028
7.1%
928
7.1%
827
6.9%
725
 
6.4%

Employed_2016
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size624.0 B
21,673
 
2
457,221
 
1
34,131
 
1
33,877
 
1
41,789
 
1
Other values (56)
56 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)96.8%

Sample

1st row152,009
2nd row18,786
3rd row563,965
4th row81,196
5th row32,768
ValueCountFrequency (%)
21,6732
 
3.2%
457,2211
 
1.6%
34,1311
 
1.6%
33,8771
 
1.6%
41,7891
 
1.6%
14,7471
 
1.6%
740,1231
 
1.6%
19,5211
 
1.6%
18,6661
 
1.6%
171,1351
 
1.6%
Other values (51)51
82.3%
2021-01-15T02:59:52.734422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,6732
 
3.2%
457,2211
 
1.6%
34,1311
 
1.6%
33,8771
 
1.6%
41,7891
 
1.6%
14,7471
 
1.6%
740,1231
 
1.6%
19,5211
 
1.6%
18,6661
 
1.6%
171,1351
 
1.6%
Other values (51)51
82.3%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
157
14.6%
242
10.8%
736
9.2%
633
8.5%
333
8.5%
830
7.7%
428
7.2%
926
6.7%
021
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
157
17.5%
242
12.9%
736
11.0%
633
10.1%
333
10.1%
830
9.2%
428
8.6%
926
8.0%
021
 
6.4%
520
 
6.1%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
157
14.6%
242
10.8%
736
9.2%
633
8.5%
333
8.5%
830
7.7%
428
7.2%
926
6.7%
021
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
157
14.6%
242
10.8%
736
9.2%
633
8.5%
333
8.5%
830
7.7%
428
7.2%
926
6.7%
021
 
5.4%

Unemployed_2016
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
3,439
 
1
1,328
 
1
52,705
 
1
17,065
 
1
1,309
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.903225806
Min length3

Characters and Unicode

Total characters304
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,426
2nd row1,266
3rd row43,330
4th row4,655
5th row2,081
ValueCountFrequency (%)
3,4391
 
1.6%
1,3281
 
1.6%
52,7051
 
1.6%
17,0651
 
1.6%
1,3091
 
1.6%
41,7331
 
1.6%
21,9131
 
1.6%
1,3631
 
1.6%
1,6821
 
1.6%
1,2751
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:53.295591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,4391
 
1.6%
1,3281
 
1.6%
52,7051
 
1.6%
17,0651
 
1.6%
1,3091
 
1.6%
41,7331
 
1.6%
21,9131
 
1.6%
1,3631
 
1.6%
1,6821
 
1.6%
1,2751
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,54
17.8%
147
15.5%
231
10.2%
329
9.5%
525
8.2%
823
7.6%
723
7.6%
420
 
6.6%
619
 
6.2%
918
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number250
82.2%
Other Punctuation54
 
17.8%

Most frequent character per category

ValueCountFrequency (%)
147
18.8%
231
12.4%
329
11.6%
525
10.0%
823
9.2%
723
9.2%
420
8.0%
619
7.6%
918
 
7.2%
015
 
6.0%
ValueCountFrequency (%)
,54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common304
100.0%

Most frequent character per script

ValueCountFrequency (%)
,54
17.8%
147
15.5%
231
10.2%
329
9.5%
525
8.2%
823
7.6%
723
7.6%
420
 
6.6%
619
 
6.2%
918
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII304
100.0%

Most frequent character per block

ValueCountFrequency (%)
,54
17.8%
147
15.5%
231
10.2%
329
9.5%
525
8.2%
823
7.6%
723
7.6%
420
 
6.6%
619
 
6.2%
918
 
5.9%

Unemployment_rate_2016
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.114516129
Minimum3.7
Maximum7.1
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:53.528501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.005
Q14.425
median5.1
Q35.7
95-th percentile6.5
Maximum7.1
Range3.4
Interquartile range (IQR)1.275

Descriptive statistics

Standard deviation0.7877851863
Coefficient of variation (CV)0.1540292701
Kurtosis-0.4511505097
Mean5.114516129
Median Absolute Deviation (MAD)0.6
Skewness0.3689465472
Sum317.1
Variance0.6206054997
MonotocityNot monotonic
2021-01-15T02:59:53.769560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5.76
 
9.7%
4.35
 
8.1%
5.35
 
8.1%
4.83
 
4.8%
4.43
 
4.8%
4.93
 
4.8%
5.43
 
4.8%
4.63
 
4.8%
5.13
 
4.8%
5.83
 
4.8%
Other values (18)25
40.3%
ValueCountFrequency (%)
3.71
1.6%
3.81
1.6%
3.91
1.6%
41
1.6%
4.11
1.6%
ValueCountFrequency (%)
7.11
1.6%
6.71
1.6%
6.61
1.6%
6.52
3.2%
6.31
1.6%

Civilian_labor_force_2017
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
17,163
 
1
42,668
 
1
180,708
 
1
143,315
 
1
31,841
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.306451613
Min length5

Characters and Unicode

Total characters391
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row158,138
2nd row19,695
3rd row605,491
4th row84,531
5th row34,282
ValueCountFrequency (%)
17,1631
 
1.6%
42,6681
 
1.6%
180,7081
 
1.6%
143,3151
 
1.6%
31,8411
 
1.6%
50,8611
 
1.6%
31,1071
 
1.6%
219,7201
 
1.6%
29,5311
 
1.6%
1,209,2841
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:54.353508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17,1631
 
1.6%
42,6681
 
1.6%
180,7081
 
1.6%
143,3151
 
1.6%
31,8411
 
1.6%
50,8611
 
1.6%
31,1071
 
1.6%
219,7201
 
1.6%
29,5311
 
1.6%
1,209,2841
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
152
13.3%
246
11.8%
336
9.2%
932
8.2%
531
7.9%
827
6.9%
627
6.9%
427
6.9%
725
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
152
15.9%
246
14.1%
336
11.0%
932
9.8%
531
9.5%
827
8.3%
627
8.3%
427
8.3%
725
7.6%
024
7.3%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
152
13.3%
246
11.8%
336
9.2%
932
8.2%
531
7.9%
827
6.9%
627
6.9%
427
6.9%
725
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII391
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
152
13.3%
246
11.8%
336
9.2%
932
8.2%
531
7.9%
827
6.9%
627
6.9%
427
6.9%
725
 
6.4%

Employed_2017
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
28,123
 
1
52,119
 
1
79,840
 
1
459,137
 
1
40,431
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,479
2nd row18,395
3rd row567,233
4th row79,840
5th row32,162
ValueCountFrequency (%)
28,1231
 
1.6%
52,1191
 
1.6%
79,8401
 
1.6%
459,1371
 
1.6%
40,4311
 
1.6%
13,8641
 
1.6%
146,8781
 
1.6%
741,9441
 
1.6%
47,7521
 
1.6%
1,152,0391
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:54.922019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28,1231
 
1.6%
52,1191
 
1.6%
79,8401
 
1.6%
459,1371
 
1.6%
40,4311
 
1.6%
13,8641
 
1.6%
146,8781
 
1.6%
741,9441
 
1.6%
47,7521
 
1.6%
1,152,0391
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
161
15.6%
241
10.5%
340
10.3%
437
9.5%
729
7.4%
928
7.2%
625
 
6.4%
025
 
6.4%
520
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
161
18.7%
241
12.6%
340
12.3%
437
11.3%
729
8.9%
928
8.6%
625
7.7%
025
7.7%
520
 
6.1%
820
 
6.1%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
161
15.6%
241
10.5%
340
10.3%
437
9.5%
729
7.4%
928
7.2%
625
 
6.4%
025
 
6.4%
520
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
161
15.6%
241
10.5%
340
10.3%
437
9.5%
729
7.4%
928
7.2%
625
 
6.4%
025
 
6.4%
520
 
5.1%

Unemployed_2017
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,300
 
1
2,120
 
1
5,070
 
1
6,142
 
1
4,687
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.951612903
Min length3

Characters and Unicode

Total characters307
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row6,659
2nd row1,300
3rd row38,258
4th row4,691
5th row2,120
ValueCountFrequency (%)
1,3001
 
1.6%
2,1201
 
1.6%
5,0701
 
1.6%
6,1421
 
1.6%
4,6871
 
1.6%
6,6591
 
1.6%
1,9001
 
1.6%
1711
 
1.6%
9371
 
1.6%
1,1761
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:55.478382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,3001
 
1.6%
2,1201
 
1.6%
5,0701
 
1.6%
6,1421
 
1.6%
4,6871
 
1.6%
6,6591
 
1.6%
1,9001
 
1.6%
1711
 
1.6%
9371
 
1.6%
1,1761
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,55
17.9%
148
15.6%
330
9.8%
227
8.8%
025
8.1%
424
7.8%
823
7.5%
622
 
7.2%
721
 
6.8%
517
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number252
82.1%
Other Punctuation55
 
17.9%

Most frequent character per category

ValueCountFrequency (%)
148
19.0%
330
11.9%
227
10.7%
025
9.9%
424
9.5%
823
9.1%
622
8.7%
721
8.3%
517
 
6.7%
915
 
6.0%
ValueCountFrequency (%)
,55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common307
100.0%

Most frequent character per script

ValueCountFrequency (%)
,55
17.9%
148
15.6%
330
9.8%
227
8.8%
025
8.1%
424
7.8%
823
7.5%
622
 
7.2%
721
 
6.8%
517
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII307
100.0%

Most frequent character per block

ValueCountFrequency (%)
,55
17.9%
148
15.6%
330
9.8%
227
8.8%
025
8.1%
424
7.8%
823
7.5%
622
 
7.2%
721
 
6.8%
517
 
5.5%

Unemployment_rate_2017
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.182258065
Minimum3.8
Maximum7.4
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:55.714041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.1
Q14.5
median5.05
Q35.7
95-th percentile6.595
Maximum7.4
Range3.6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.807246726
Coefficient of variation (CV)0.1557712325
Kurtosis-0.3760938235
Mean5.182258065
Median Absolute Deviation (MAD)0.55
Skewness0.4876480084
Sum321.3
Variance0.6516472766
MonotocityNot monotonic
2021-01-15T02:59:55.949466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4.57
 
11.3%
5.56
 
9.7%
55
 
8.1%
4.73
 
4.8%
5.73
 
4.8%
4.33
 
4.8%
4.13
 
4.8%
5.83
 
4.8%
5.33
 
4.8%
4.22
 
3.2%
Other values (17)24
38.7%
ValueCountFrequency (%)
3.81
 
1.6%
41
 
1.6%
4.13
4.8%
4.22
3.2%
4.33
4.8%
ValueCountFrequency (%)
7.41
1.6%
6.71
1.6%
6.62
3.2%
6.52
3.2%
6.31
1.6%

Civilian_labor_force_2018
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
99,042
 
1
42,613
 
1
22,426
 
1
482,058
 
1
183,560
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.306451613
Min length5

Characters and Unicode

Total characters391
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row157,520
2nd row19,481
3rd row599,929
4th row84,140
5th row34,042
ValueCountFrequency (%)
99,0421
 
1.6%
42,6131
 
1.6%
22,4261
 
1.6%
482,0581
 
1.6%
183,5601
 
1.6%
22,8121
 
1.6%
222,7711
 
1.6%
1,141,8301
 
1.6%
43,3821
 
1.6%
35,9771
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:56.525732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
99,0421
 
1.6%
42,6131
 
1.6%
22,4261
 
1.6%
482,0581
 
1.6%
183,5601
 
1.6%
22,8121
 
1.6%
222,7711
 
1.6%
1,141,8301
 
1.6%
43,3821
 
1.6%
35,9771
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
151
13.0%
248
12.3%
433
8.4%
331
7.9%
729
7.4%
929
7.4%
628
7.2%
827
6.9%
526
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
151
15.6%
248
14.7%
433
10.1%
331
9.5%
729
8.9%
929
8.9%
628
8.6%
827
8.3%
526
8.0%
025
7.6%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
151
13.0%
248
12.3%
433
8.4%
331
7.9%
729
7.4%
929
7.4%
628
7.2%
827
6.9%
526
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII391
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
151
13.0%
248
12.3%
433
8.4%
331
7.9%
729
7.4%
929
7.4%
628
7.2%
827
6.9%
526
6.6%

Employed_2018
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
21,285
 
1
73,107
 
1
48,205
 
1
21,869
 
1
16,222
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,688
2nd row18,390
3rd row565,365
4th row80,039
5th row32,225
ValueCountFrequency (%)
21,2851
 
1.6%
73,1071
 
1.6%
48,2051
 
1.6%
21,8691
 
1.6%
16,2221
 
1.6%
17,1351
 
1.6%
422,6091
 
1.6%
84,8171
 
1.6%
681,6861
 
1.6%
28,4911
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:57.090672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,2851
 
1.6%
73,1071
 
1.6%
48,2051
 
1.6%
21,8691
 
1.6%
16,2221
 
1.6%
17,1351
 
1.6%
422,6091
 
1.6%
84,8171
 
1.6%
681,6861
 
1.6%
28,4911
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
153
13.6%
234
8.7%
033
8.5%
332
8.2%
631
7.9%
431
7.9%
530
7.7%
829
7.4%
927
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
153
16.3%
234
10.4%
033
10.1%
332
9.8%
631
9.5%
431
9.5%
530
9.2%
829
8.9%
927
8.3%
726
8.0%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
153
13.6%
234
8.7%
033
8.5%
332
8.2%
631
7.9%
431
7.9%
530
7.7%
829
7.4%
927
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
153
13.6%
234
8.7%
033
8.5%
332
8.2%
631
7.9%
431
7.9%
530
7.7%
829
7.4%
927
6.9%

Unemployed_2018
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
2,760
 
1
29,873
 
1
34,089
 
1
1,563
 
1
445
 
1
Other values (57)
57 

Length

Max length6
Median length5
Mean length4.758064516
Min length3

Characters and Unicode

Total characters295
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row5,832
2nd row1,091
3rd row34,564
4th row4,101
5th row1,817
ValueCountFrequency (%)
2,7601
 
1.6%
29,8731
 
1.6%
34,0891
 
1.6%
1,5631
 
1.6%
4451
 
1.6%
9411
 
1.6%
9991
 
1.6%
1,3271
 
1.6%
8341
 
1.6%
1,0911
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:57.666205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,7601
 
1.6%
29,8731
 
1.6%
34,0891
 
1.6%
1,5631
 
1.6%
4451
 
1.6%
9411
 
1.6%
9991
 
1.6%
1,3271
 
1.6%
8341
 
1.6%
1,0911
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
151
17.3%
,50
16.9%
429
9.8%
226
8.8%
825
8.5%
521
7.1%
621
7.1%
319
 
6.4%
919
 
6.4%
017
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number245
83.1%
Other Punctuation50
 
16.9%

Most frequent character per category

ValueCountFrequency (%)
151
20.8%
429
11.8%
226
10.6%
825
10.2%
521
8.6%
621
8.6%
319
 
7.8%
919
 
7.8%
017
 
6.9%
717
 
6.9%
ValueCountFrequency (%)
,50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common295
100.0%

Most frequent character per script

ValueCountFrequency (%)
151
17.3%
,50
16.9%
429
9.8%
226
8.8%
825
8.5%
521
7.1%
621
7.1%
319
 
6.4%
919
 
6.4%
017
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII295
100.0%

Most frequent character per block

ValueCountFrequency (%)
151
17.3%
,50
16.9%
429
9.8%
226
8.8%
825
8.5%
521
7.1%
621
7.1%
319
 
6.4%
919
 
6.4%
017
 
5.8%

Unemployment_rate_2018
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.503225806
Minimum3.3
Maximum6.9
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T02:59:57.884969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile3.605
Q13.9
median4.4
Q34.975
95-th percentile5.6
Maximum6.9
Range3.6
Interquartile range (IQR)1.075

Descriptive statistics

Standard deviation0.7045444388
Coefficient of variation (CV)0.156453278
Kurtosis0.7370971347
Mean4.503225806
Median Absolute Deviation (MAD)0.5
Skewness0.711309404
Sum279.2
Variance0.4963828662
MonotocityNot monotonic
2021-01-15T02:59:58.124776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3.96
 
9.7%
3.76
 
9.7%
4.84
 
6.5%
4.44
 
6.5%
5.64
 
6.5%
5.14
 
6.5%
4.23
 
4.8%
4.93
 
4.8%
4.33
 
4.8%
4.63
 
4.8%
Other values (14)22
35.5%
ValueCountFrequency (%)
3.31
 
1.6%
3.52
 
3.2%
3.61
 
1.6%
3.76
9.7%
3.82
 
3.2%
ValueCountFrequency (%)
6.91
 
1.6%
5.81
 
1.6%
5.64
6.5%
5.51
 
1.6%
5.31
 
1.6%

Civilian_labor_force_2019
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
19,441
 
1
20,271
 
1
14,637
 
1
22,306
 
1
18,131
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.306451613
Min length5

Characters and Unicode

Total characters391
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row157,444
2nd row19,441
3rd row597,677
4th row83,444
5th row33,884
ValueCountFrequency (%)
19,4411
 
1.6%
20,2711
 
1.6%
14,6371
 
1.6%
22,3061
 
1.6%
18,1311
 
1.6%
54,5501
 
1.6%
76,0281
 
1.6%
52,9601
 
1.6%
17,5671
 
1.6%
31,1811
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:58.722188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19,4411
 
1.6%
20,2711
 
1.6%
14,6371
 
1.6%
22,3061
 
1.6%
18,1311
 
1.6%
54,5501
 
1.6%
76,0281
 
1.6%
52,9601
 
1.6%
17,5671
 
1.6%
31,1811
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
153
13.6%
243
11.0%
335
9.0%
433
8.4%
930
7.7%
830
7.7%
628
7.2%
526
6.6%
725
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number327
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
153
16.2%
243
13.1%
335
10.7%
433
10.1%
930
9.2%
830
9.2%
628
8.6%
526
8.0%
725
7.6%
024
7.3%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
153
13.6%
243
11.0%
335
9.0%
433
8.4%
930
7.7%
830
7.7%
628
7.2%
526
6.6%
725
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII391
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
153
13.6%
243
11.0%
335
9.0%
433
8.4%
930
7.7%
830
7.7%
628
7.2%
526
6.6%
725
 
6.4%

Employed_2019
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
29,423
 
1
466,158
 
1
28,564
 
1
79,488
 
1
96,839
 
1
Other values (57)
57 

Length

Max length9
Median length6
Mean length6.290322581
Min length5

Characters and Unicode

Total characters390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row151,699
2nd row18,380
3rd row565,642
4th row79,488
5th row32,172
ValueCountFrequency (%)
29,4231
 
1.6%
466,1581
 
1.6%
28,5641
 
1.6%
79,4881
 
1.6%
96,8391
 
1.6%
41,4701
 
1.6%
93,8871
 
1.6%
51,9761
 
1.6%
40,9021
 
1.6%
13,9541
 
1.6%
Other values (52)52
83.9%
2021-01-15T02:59:59.283976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29,4231
 
1.6%
466,1581
 
1.6%
28,5641
 
1.6%
79,4881
 
1.6%
96,8391
 
1.6%
41,4701
 
1.6%
93,8871
 
1.6%
51,9761
 
1.6%
40,9021
 
1.6%
13,9541
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,64
16.4%
148
12.3%
434
8.7%
734
8.7%
633
8.5%
332
8.2%
232
8.2%
931
7.9%
829
7.4%
029
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number326
83.6%
Other Punctuation64
 
16.4%

Most frequent character per category

ValueCountFrequency (%)
148
14.7%
434
10.4%
734
10.4%
633
10.1%
332
9.8%
232
9.8%
931
9.5%
829
8.9%
029
8.9%
524
7.4%
ValueCountFrequency (%)
,64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390
100.0%

Most frequent character per script

ValueCountFrequency (%)
,64
16.4%
148
12.3%
434
8.7%
734
8.7%
633
8.5%
332
8.2%
232
8.2%
931
7.9%
829
7.4%
029
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII390
100.0%

Most frequent character per block

ValueCountFrequency (%)
,64
16.4%
148
12.3%
434
8.7%
734
8.7%
633
8.5%
332
8.2%
232
8.2%
931
7.9%
829
7.4%
029
7.4%

Unemployed_2019
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size624.0 B
1,133
 
2
32,035
 
1
31,973
 
1
24,392
 
1
913
 
1
Other values (56)
56 

Length

Max length6
Median length5
Mean length4.693548387
Min length3

Characters and Unicode

Total characters291
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)96.8%

Sample

1st row5,745
2nd row1,061
3rd row32,035
4th row3,956
5th row1,712
ValueCountFrequency (%)
1,1332
 
3.2%
32,0351
 
1.6%
31,9731
 
1.6%
24,3921
 
1.6%
9131
 
1.6%
1,9481
 
1.6%
1,7651
 
1.6%
1,5411
 
1.6%
1,7941
 
1.6%
1331
 
1.6%
Other values (51)51
82.3%
2021-01-15T02:59:59.850593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,1332
 
3.2%
32,0351
 
1.6%
31,9731
 
1.6%
24,3921
 
1.6%
9131
 
1.6%
1,9481
 
1.6%
1,7651
 
1.6%
1,5411
 
1.6%
1,7941
 
1.6%
1331
 
1.6%
Other values (51)51
82.3%

Most occurring characters

ValueCountFrequency (%)
,48
16.5%
146
15.8%
332
11.0%
427
9.3%
226
8.9%
923
7.9%
822
7.6%
521
7.2%
719
 
6.5%
014
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number243
83.5%
Other Punctuation48
 
16.5%

Most frequent character per category

ValueCountFrequency (%)
146
18.9%
332
13.2%
427
11.1%
226
10.7%
923
9.5%
822
9.1%
521
8.6%
719
7.8%
014
 
5.8%
613
 
5.3%
ValueCountFrequency (%)
,48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common291
100.0%

Most frequent character per script

ValueCountFrequency (%)
,48
16.5%
146
15.8%
332
11.0%
427
9.3%
226
8.9%
923
7.9%
822
7.6%
521
7.2%
719
 
6.5%
014
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII291
100.0%

Most frequent character per block

ValueCountFrequency (%)
,48
16.5%
146
15.8%
332
11.0%
427
9.3%
226
8.9%
923
7.9%
822
7.6%
521
7.2%
719
 
6.5%
014
 
4.8%

Unemployment_rate_2019
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.338709677
Minimum3.2
Maximum6
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T03:00:00.060196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile3.5
Q13.8
median4.25
Q34.7
95-th percentile5.5
Maximum6
Range2.8
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6479108517
Coefficient of variation (CV)0.1493326127
Kurtosis-0.4611601334
Mean4.338709677
Median Absolute Deviation (MAD)0.45
Skewness0.4943146873
Sum269
Variance0.4197884717
MonotocityNot monotonic
2021-01-15T03:00:00.262473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4.77
 
11.3%
3.86
 
9.7%
4.24
 
6.5%
3.94
 
6.5%
3.64
 
6.5%
4.13
 
4.8%
3.73
 
4.8%
5.53
 
4.8%
4.93
 
4.8%
4.33
 
4.8%
Other values (12)22
35.5%
ValueCountFrequency (%)
3.21
 
1.6%
3.42
3.2%
3.52
3.2%
3.64
6.5%
3.73
4.8%
ValueCountFrequency (%)
61
 
1.6%
5.61
 
1.6%
5.53
4.8%
5.42
3.2%
5.12
3.2%

Median_Household_Income_2018
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size624.0 B
50,928
 
1
62,930
 
1
54,862
 
1
53,745
 
1
55,913
 
1
Other values (57)
57 

Length

Max length7
Median length6
Mean length6.048387097
Min length6

Characters and Unicode

Total characters375
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row64,536
2nd row46,262
3rd row38,566
4th row50,928
5th row48,017
ValueCountFrequency (%)
50,9281
 
1.6%
62,9301
 
1.6%
54,8621
 
1.6%
53,7451
 
1.6%
55,9131
 
1.6%
54,0961
 
1.6%
79,7191
 
1.6%
51,9851
 
1.6%
79,6041
 
1.6%
50,3121
 
1.6%
Other values (52)52
83.9%
2021-01-15T03:00:00.785622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50,9281
 
1.6%
62,9301
 
1.6%
54,8621
 
1.6%
53,7451
 
1.6%
55,9131
 
1.6%
54,0961
 
1.6%
79,7191
 
1.6%
51,9851
 
1.6%
79,6041
 
1.6%
50,3121
 
1.6%
Other values (52)52
83.9%

Most occurring characters

ValueCountFrequency (%)
,62
16.5%
555
14.7%
635
9.3%
032
8.5%
132
8.5%
829
7.7%
428
7.5%
927
7.2%
727
7.2%
324
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number313
83.5%
Other Punctuation62
 
16.5%

Most frequent character per category

ValueCountFrequency (%)
555
17.6%
635
11.2%
032
10.2%
132
10.2%
829
9.3%
428
8.9%
927
8.6%
727
8.6%
324
7.7%
224
7.7%
ValueCountFrequency (%)
,62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common375
100.0%

Most frequent character per script

ValueCountFrequency (%)
,62
16.5%
555
14.7%
635
9.3%
032
8.5%
132
8.5%
829
7.7%
428
7.5%
927
7.2%
727
7.2%
324
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII375
100.0%

Most frequent character per block

ValueCountFrequency (%)
,62
16.5%
555
14.7%
635
9.3%
032
8.5%
132
8.5%
829
7.7%
428
7.5%
927
7.2%
727
7.2%
324
 
6.4%
Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.00806452
Minimum57
Maximum170.4
Zeros0
Zeros (%)0.0%
Memory size624.0 B
2021-01-15T03:00:01.033131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile68.48
Q177.725
median83
Q393.15
95-th percentile139.29
Maximum170.4
Range113.4
Interquartile range (IQR)15.425

Descriptive statistics

Standard deviation22.06345765
Coefficient of variation (CV)0.2451275646
Kurtosis3.065665584
Mean90.00806452
Median Absolute Deviation (MAD)7.1
Skewness1.750988956
Sum5580.5
Variance486.7961634
MonotocityNot monotonic
2021-01-15T03:00:01.320863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.82
 
3.2%
64.71
 
1.6%
83.31
 
1.6%
77.71
 
1.6%
78.31
 
1.6%
75.31
 
1.6%
68.41
 
1.6%
147.91
 
1.6%
87.21
 
1.6%
80.31
 
1.6%
Other values (51)51
82.3%
ValueCountFrequency (%)
571
1.6%
64.71
1.6%
67.51
1.6%
68.41
1.6%
701
1.6%
ValueCountFrequency (%)
170.41
1.6%
149.11
1.6%
147.91
1.6%
139.71
1.6%
131.51
1.6%

Interactions

2021-01-15T02:56:51.088477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:51.306440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:51.515562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:51.733215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:51.920544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:52.111294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:52.307390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:52.509016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:53.807278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:54.026056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:54.232851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:54.440310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:54.650455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:54.856282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:55.054797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:55.258993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:55.464540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:55.668989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:55.873252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:56.076522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:56.287268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:56.500097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:56.696157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:56.901059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:57.104068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:57.320229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:57.546177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:57.750114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:57.953283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:58.158541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:58.369743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:58.578089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:58.802202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:59.020487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:59.232956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:59.452781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:59.667385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:56:59.881082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:00.096738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:00.308723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:00.528971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:00.783140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:00.979538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:01.213266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:01.430033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:01.640982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:01.851797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:02.062324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:02.514481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:02.748472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:02.953328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:03.158023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:03.376629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:03.594316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:03.814497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:04.049272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:04.274248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:04.491614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:04.715665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:04.936543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:05.150677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:05.380337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:05.596783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:05.821774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:06.036362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:06.259900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:06.489693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:06.714975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:06.939643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:07.154532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:07.376173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:07.605274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:07.835439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:08.053712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:08.264649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:08.488400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:08.716358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:08.936390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:09.181357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:09.409811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:09.640431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:09.873470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:10.100448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:10.328221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:10.553964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:10.783523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:11.016223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:11.239260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:11.463672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:11.695970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:11.929970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:12.147913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:12.381659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:12.565628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:13.038340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:13.240559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:13.449259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:13.626474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:13.811440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.005425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.190886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.401831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.592876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.784377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:14.988343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:15.180529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:15.373191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:15.568486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:15.757673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:15.958618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:16.144806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:16.339245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:16.543376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:16.741647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:16.929894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:17.124788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:17.320770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:17.515514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:17.716504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:17.926901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:18.114363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:18.301901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:18.494715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:18.682465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:18.886913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:19.078628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:19.276196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:19.473470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:19.664528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:19.855991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:20.047681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:20.243528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:20.441553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:20.628028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:20.826432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:21.024685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:21.224441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:21.418267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:21.608927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:21.805491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:22.005311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:22.211270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:22.428513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:22.613319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:22.798999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:23.003810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:23.200886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:23.418982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:23.621230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:23.822901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:24.035345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:24.235654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:24.441779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:24.638822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:24.836553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:25.349627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:25.551299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:25.753177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:25.960629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:26.171968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:26.371188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:26.576765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:26.778404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:26.986801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:27.198293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:27.417034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:27.618435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:27.812693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:28.015571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:28.219829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:28.442434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:28.653957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:28.867678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:29.083387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:29.295886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:29.502454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:29.710515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:29.928694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:30.142441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:30.352480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:30.567282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:30.786236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:30.976073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:31.180351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:31.400606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:31.596487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:31.801616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:32.014601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:32.226602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:32.431459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:32.619081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:32.812250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:33.016068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:33.231643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:33.440461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:33.644663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:33.855345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:34.063454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:34.263063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:34.467145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:34.670946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:34.878122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:35.080259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:35.286626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:35.500778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:35.716156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:35.917500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:36.124511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:36.356735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:36.594931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:36.839506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:37.080177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:37.305063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:37.523653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:37.750903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:37.981485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:38.213759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:38.448596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:38.680578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:38.925922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:39.161930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:39.401077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:39.633645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:39.871993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:40.144296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:40.387549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:41.029585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:41.271520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:41.518636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:41.744950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:42.011914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:42.256034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:42.485696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:42.704843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:42.926785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:43.139151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:43.340086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:43.556226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:43.768758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:43.976315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:44.211724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:44.429641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:44.654232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:44.868251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:45.083820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:45.298632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:45.511621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:45.737463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:45.947886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:46.167968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:46.389778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:46.614820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:46.830951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:47.043550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:47.256974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:47.475623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:47.700567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:47.931451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:48.136928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:48.341444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:48.546957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:48.759480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:48.975085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:49.203878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:49.424991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:49.646620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:49.865686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:50.081897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:50.294698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:50.512763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:50.734093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:50.947159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:51.163761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:51.392479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:51.618533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:51.826038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:52.046062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:52.273470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:52.503606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:52.737618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:52.972575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:53.182438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:53.393059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:53.614842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:53.835255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:54.053735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:54.296244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:54.523974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:54.753525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:54.977083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:55.203497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:55.427514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:55.650923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:55.888068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:56.105295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:56.335043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:56.566086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:56.801088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:57.019259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:57.239583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:57.487168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:57.702453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:57.927583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:58.152097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:58.352990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:58.560088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:58.766306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:58.983893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:59.186494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:59.417249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:59.636500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:57:59.852702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:00.081906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:00.787234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:00.972996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:01.185144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:01.407737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:01.619123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:01.832908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:02.055746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:02.274764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:02.486967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:02.704496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:02.914509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:03.135682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:03.354957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:03.577932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:03.778023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:03.979784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:04.191359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:04.406112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:04.613646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:04.844943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:05.057341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:05.277820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:05.497708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:05.707738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:05.925990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:06.135721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:06.359347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:06.566693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:06.784331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:07.013282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:07.233971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:07.451412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:07.674443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:07.900981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:08.123670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:08.351792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:08.593230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:08.801072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:09.017890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:09.226127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:09.440885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:09.652283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:09.880341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:10.101748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:10.316186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:10.546731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:10.758834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:10.975335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:11.191832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:11.415651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:11.626263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:11.843660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:12.065141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:12.293846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:12.504958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:12.721819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:12.929143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:13.147754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:13.372893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:13.593143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:13.799882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:14.004240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:14.213912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:14.434919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:14.642420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:14.873895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:15.090132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:15.308955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:15.533842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:15.748447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:15.969173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:16.183105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:16.410396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:16.617362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:16.831758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:17.066283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:17.290812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:17.509032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:17.728792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:17.947938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:18.177750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:18.410416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:18.653769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:18.864754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:19.081661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:19.300009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:19.528080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:19.749720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:19.990889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:20.219146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:20.445228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:20.682711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:20.908815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:21.131257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:21.354641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:21.580703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:21.805166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:22.032717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:22.269722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:22.514948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:22.742854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:22.972170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:23.174725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:23.391836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:23.617057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:23.836142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:24.029770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:24.818254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:25.029226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:25.237984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:25.444223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:25.664714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:25.880760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:26.093749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:26.305287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:26.516489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:26.716134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:26.916389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:27.125166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:27.336753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:27.545668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:27.760733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:27.971710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:28.177894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:28.383243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:28.598336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:28.821875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:29.049786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:29.281863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:29.488270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:29.696012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:29.907799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:30.130845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:30.347172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:30.582325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:30.806335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:30.997195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:31.227421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:31.447072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:31.665884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:31.889633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:32.110371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:32.340831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:32.551346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:32.779355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:33.008926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:33.218748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:33.441647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:33.667849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:33.897644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:34.129952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:34.362461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:34.577333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:34.790623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:35.019136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:35.243912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:35.473039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:35.711964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:35.936946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:36.167097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:36.399560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:36.627230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:36.848914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:37.074092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:37.294202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:37.527020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:37.753935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:37.983471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:38.220493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:38.446938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:38.680429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:38.904503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:39.134941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:39.420990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:39.681113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:39.894991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:40.115865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:40.338526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:40.566862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:40.789932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:41.027900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:41.254345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:41.490267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:41.721366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:41.951641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:42.185483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:42.413948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:42.643956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:42.874492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:43.100655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:43.331888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:43.569139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:43.791170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:44.021919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:44.219383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:44.427133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:44.649032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:44.870038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:45.076085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:45.269582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:45.472590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:45.697483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:45.896449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:46.132216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:46.351966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:46.566040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:46.784235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:46.991303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:47.208086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:47.412054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:47.619167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:47.838123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:48.040948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:48.251984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:48.468187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:48.686929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:48.900567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:49.105450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:49.321374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:49.537976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:49.773449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:49.978273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:50.185332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:50.400172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:50.617113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:50.821179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:51.054594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:51.271466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:51.503582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:51.726162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:51.946990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:52.162632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:52.375312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:52.592270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:52.809699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:53.020577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:53.238176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:53.466634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T02:58:53.693657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-15T03:00:01.669853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-15T03:00:02.445197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-15T03:00:03.208608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-15T03:00:04.238681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-15T03:00:09.307497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-15T02:58:55.507757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-15T02:59:07.720637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexFIPStxtStabrarea_nameRural_urban_continuum_code_2013Urban_influence_code_2013Metro_2013Civilian_labor_force_2000Employed_2000Unemployed_2000Unemployment_rate_2000Civilian_labor_force_2001Employed_2001Unemployed_2001Unemployment_rate_2001Civilian_labor_force_2002Employed_2002Unemployed_2002Unemployment_rate_2002Civilian_labor_force_2003Employed_2003Unemployed_2003Unemployment_rate_2003Civilian_labor_force_2004Employed_2004Unemployed_2004Unemployment_rate_2004Civilian_labor_force_2005Employed_2005Unemployed_2005Unemployment_rate_2005Civilian_labor_force_2006Employed_2006Unemployed_2006Unemployment_rate_2006Civilian_labor_force_2007Employed_2007Unemployed_2007Unemployment_rate_2007Civilian_labor_force_2008Employed_2008Unemployed_2008Unemployment_rate_2008Civilian_labor_force_2009Employed_2009Unemployed_2009Unemployment_rate_2009Civilian_labor_force_2010Employed_2010Unemployed_2010Unemployment_rate_2010Civilian_labor_force_2011Employed_2011Unemployed_2011Unemployment_rate_2011Civilian_labor_force_2012Employed_2012Unemployed_2012Unemployment_rate_2012Civilian_labor_force_2013Employed_2013Unemployed_2013Unemployment_rate_2013Civilian_labor_force_2014Employed_2014Unemployed_2014Unemployment_rate_2014Civilian_labor_force_2015Employed_2015Unemployed_2015Unemployment_rate_2015Civilian_labor_force_2016Employed_2016Unemployed_2016Unemployment_rate_2016Civilian_labor_force_2017Employed_2017Unemployed_2017Unemployment_rate_2017Civilian_labor_force_2018Employed_2018Unemployed_2018Unemployment_rate_2018Civilian_labor_force_2019Employed_2019Unemployed_2019Unemployment_rate_2019Median_Household_Income_2018Med_HH_Income_Percent_of_State_Total_2018
0186436001NYAlbany County, NY2.02.01.0153,997148,8135,1843.4154,732149,5645,1683.3157,694151,5366,1583.9157,860151,2006,6604.2158,668152,0456,6234.2159,258153,0406,2183.9159,472153,3856,0873.8158,712152,5676,1453.9160,552152,7567,7964.9158,937148,18310,7546.8161,511150,26411,2477.0158,289147,33010,9596.9160,657149,26011,3977.1160,805151,0599,7466.1157,231149,5377,6944.9158,470151,5846,8864.0158,435152,0096,4264.1158,138151,4796,6594.2157,520151,6885,8323.7157,444151,6995,7453.664,53695.4
1186536003NYAllegany County, NY7.09.00.022,65921,5441,1154.922,99821,9081,0904.723,44922,1921,2575.423,39921,8811,5186.523,57822,0711,5076.423,53222,1631,3695.823,52122,3061,2155.223,40522,1291,2765.524,12322,5781,5456.423,89821,8162,0828.724,24022,0222,2189.223,65721,5452,1128.923,69121,6971,9948.423,79922,0141,7857.523,31721,8661,4516.220,34119,0191,3227.020,05218,7861,2666.319,69518,3951,3006.619,48118,3901,0915.619,44118,3801,0615.546,26268.4
2186636005NYBronx County, NY1.01.01.0486,621452,03634,5857.1488,643453,13835,5057.3496,904448,50448,4009.7497,689444,90152,78810.6502,064455,83746,2279.2507,229468,80938,4207.6505,378471,74833,6306.7512,479477,48734,9926.8523,746484,14539,6017.6533,866469,62464,24212.0582,287512,47769,81012.0584,353514,63569,71811.9594,943520,98773,95612.4603,461532,42871,03311.8603,659544,53559,1249.8605,128558,07547,0538.0607,295563,96543,3307.1605,491567,23338,2586.3599,929565,36534,5645.8597,677565,64232,0355.438,56657.0
3186736007NYBroome County, NY2.02.01.097,73494,1553,5793.798,08893,9254,1634.298,78193,0885,6935.896,72291,1385,5845.896,28391,0755,2085.496,28791,5854,7024.996,99892,5454,4534.696,85892,5494,3094.498,16092,6905,4705.697,50089,5537,9478.297,84489,3168,5288.794,72786,5798,1488.694,01285,8358,1778.791,93484,7797,1557.887,97482,1285,8466.686,78181,5995,1826.085,85181,1964,6555.484,53179,8404,6915.584,14080,0394,1014.983,44479,4883,9564.750,92875.3
4186836009NYCattaraugus County, NY4.03.00.040,86739,0021,8654.641,02138,9592,0625.041,75539,4272,3285.642,15739,7252,4325.842,27039,7402,5306.042,12339,8582,2655.441,92539,7882,1375.141,26939,1642,1055.141,76639,2222,5446.141,66838,0793,5898.639,65435,8813,7739.538,54034,9433,5979.338,30834,7333,5759.338,10034,8513,2498.536,61834,0522,5667.035,21232,9982,2146.034,84932,7682,0816.034,28232,1622,1206.234,04232,2251,8175.333,88432,1721,7125.148,01771.0
5186936011NYCayuga County, NY4.05.00.040,19438,6131,5813.939,91238,2091,7034.341,22939,2591,9704.841,38739,2212,1665.241,75439,6182,1365.141,86839,8352,0334.941,49939,5661,9334.741,13739,3121,8254.441,80239,4822,3205.541,59038,1573,4338.339,88536,4803,4058.539,06835,8993,1698.139,11335,8843,2298.338,85435,9892,8657.437,60635,3522,2546.037,06635,0881,9785.036,69134,8361,8555.135,87534,0771,7985.035,97734,3701,6074.535,95234,4111,5414.352,94578.3
6187036013NYChautauqua County, NY4.03.00.067,73364,9862,7474.166,93663,8243,1124.667,43163,7013,7305.566,73562,9093,8265.766,93063,2773,6535.567,14963,9293,2204.867,13564,1263,0094.566,78163,7902,9914.567,32363,5913,7325.565,93460,4675,4678.363,92258,2625,6608.961,66156,6165,0458.261,27856,0555,2238.560,21855,3874,8318.057,76453,8273,9376.857,59354,1243,4696.056,11352,8573,2565.855,43352,1193,3146.054,77952,0192,7605.054,55051,9762,5744.745,68967.5
7187136015NYChemung County, NY3.02.01.042,54440,6951,8494.342,36740,2742,0934.942,26939,5752,6946.441,18738,5502,6376.440,51538,0842,4316.040,59538,4382,1575.340,85138,8791,9724.840,95639,0801,8764.641,45939,1472,3125.641,01637,3643,6528.941,59438,1053,4898.440,80337,6063,1977.840,55337,1423,4118.439,60936,4793,1307.938,16235,7722,3906.337,46635,2692,1976.036,20234,1312,0715.735,66733,7051,9625.535,35333,7181,6354.635,01933,5431,4764.252,56577.7
8187236017NYChenango County, NY6.06.00.024,37323,3909834.024,51423,4111,1034.524,61923,2591,3605.524,21922,8201,3995.824,09022,7551,3355.524,29123,0921,1994.924,71223,5141,1984.824,40523,2001,2054.924,67223,1041,5686.424,40722,2312,1768.923,69121,5092,1829.223,83021,7602,0708.724,12722,0442,0838.624,09722,3351,7627.323,17821,7371,4416.223,08621,8261,2606.022,82921,6731,1565.122,33121,1411,1905.322,32721,2851,0424.722,08021,0989824.450,31274.4
9187336019NYClinton County, NY5.08.00.038,37536,5681,8074.738,76837,0421,7264.539,65737,5492,1085.339,87837,4842,3946.039,89037,4772,4136.040,01837,9152,1035.340,06437,9322,1325.339,78437,5562,2285.639,98837,3702,6186.539,78736,0843,7039.339,70135,8093,8929.838,41334,7293,6849.637,99834,4243,5749.437,17634,0853,0918.335,97533,5952,3806.635,91833,8282,0906.035,79533,8771,9185.436,13934,2391,9005.336,26734,6771,5904.436,30234,7201,5824.455,51782.1

Last rows

df_indexFIPStxtStabrarea_nameRural_urban_continuum_code_2013Urban_influence_code_2013Metro_2013Civilian_labor_force_2000Employed_2000Unemployed_2000Unemployment_rate_2000Civilian_labor_force_2001Employed_2001Unemployed_2001Unemployment_rate_2001Civilian_labor_force_2002Employed_2002Unemployed_2002Unemployment_rate_2002Civilian_labor_force_2003Employed_2003Unemployed_2003Unemployment_rate_2003Civilian_labor_force_2004Employed_2004Unemployed_2004Unemployment_rate_2004Civilian_labor_force_2005Employed_2005Unemployed_2005Unemployment_rate_2005Civilian_labor_force_2006Employed_2006Unemployed_2006Unemployment_rate_2006Civilian_labor_force_2007Employed_2007Unemployed_2007Unemployment_rate_2007Civilian_labor_force_2008Employed_2008Unemployed_2008Unemployment_rate_2008Civilian_labor_force_2009Employed_2009Unemployed_2009Unemployment_rate_2009Civilian_labor_force_2010Employed_2010Unemployed_2010Unemployment_rate_2010Civilian_labor_force_2011Employed_2011Unemployed_2011Unemployment_rate_2011Civilian_labor_force_2012Employed_2012Unemployed_2012Unemployment_rate_2012Civilian_labor_force_2013Employed_2013Unemployed_2013Unemployment_rate_2013Civilian_labor_force_2014Employed_2014Unemployed_2014Unemployment_rate_2014Civilian_labor_force_2015Employed_2015Unemployed_2015Unemployment_rate_2015Civilian_labor_force_2016Employed_2016Unemployed_2016Unemployment_rate_2016Civilian_labor_force_2017Employed_2017Unemployed_2017Unemployment_rate_2017Civilian_labor_force_2018Employed_2018Unemployed_2018Unemployment_rate_2018Civilian_labor_force_2019Employed_2019Unemployed_2019Unemployment_rate_2019Median_Household_Income_2018Med_HH_Income_Percent_of_State_Total_2018
52191636105NYSullivan County, NY4.04.00.032,97131,5231,4484.433,26231,7531,5094.534,32632,6231,7035.034,39032,5971,7935.234,92333,0731,8505.335,15933,4561,7034.835,38333,6111,7725.035,22733,3911,8365.235,66133,3392,3226.535,38132,3093,0728.736,47533,2263,2498.935,48032,3353,1458.935,31132,0933,2189.134,58831,7782,8108.133,26931,0822,1876.633,71031,8911,8195.034,02532,3991,6264.834,29632,6211,6754.935,92734,4541,4734.136,65335,2051,4484.051,98576.8
53191736107NYTioga County, NY2.02.01.026,39825,4829163.526,46725,3801,0874.126,72925,2721,4575.526,31824,8421,4765.626,14024,7791,3615.226,31025,0691,2414.726,45425,3251,1294.326,17924,9521,2274.726,36724,9731,3945.326,24424,1242,1208.126,56824,4482,1208.025,81823,8321,9867.725,46623,4572,0097.924,97223,1941,7787.123,77522,3231,4526.123,38422,0881,2966.023,01021,8291,1815.122,74521,5801,1655.122,86821,8699994.422,68721,7469414.160,30989.2
54191836109NYTompkins County, NY3.02.01.050,53848,8721,6663.351,24349,5181,7253.452,57350,4442,1294.053,28851,2002,0883.954,01051,8372,1734.054,60052,6531,9473.655,37853,5061,8723.455,35153,4851,8663.456,22253,8962,3264.156,66153,3443,3175.954,76751,3973,3706.254,19050,9103,2806.155,07451,6993,3756.155,92453,0122,9125.254,74652,3422,4044.450,42348,2072,2164.050,35148,2572,0944.249,91247,7522,1604.350,01648,2051,8113.649,36147,5671,7943.657,38384.8
55191936111NYUlster County, NY3.02.01.088,05984,9003,1593.688,19184,8503,3413.890,79086,8173,9734.491,39787,2464,1514.591,37986,9454,4344.991,81687,8283,9884.392,55188,7443,8074.190,48586,5553,9304.390,71585,7704,9455.589,94483,0146,9307.793,60686,3107,2967.891,28984,1237,1667.891,04583,5087,5378.390,35083,8996,4517.187,97582,9585,0175.788,33984,0974,2425.088,13884,2423,8964.488,04184,0473,9944.588,26884,8173,4513.987,69284,3873,3053.863,07393.2
56192036113NYWarren County, NY3.02.01.033,15231,7631,3894.233,28031,8091,4714.433,75332,0831,6704.934,47832,7291,7495.134,99933,2861,7134.935,47133,8521,6194.636,50234,8761,6264.536,32434,7061,6184.536,68934,6242,0655.636,43533,5572,8787.934,15431,0823,0729.033,61930,6193,0008.933,76130,6603,1019.233,14730,4372,7108.232,29330,1632,1306.632,25530,4711,7846.031,98030,2981,6825.331,84130,1681,6735.331,52830,0591,4694.731,29929,8681,4314.656,69483.8
57192136115NYWashington County, NY3.02.01.029,98128,8591,1223.730,09828,8881,2104.030,42229,0301,3924.630,96329,5611,4024.531,79630,2991,4974.732,14430,7081,4364.532,88731,5501,3374.132,40331,0601,3434.132,85031,0991,7515.332,51030,0922,4187.430,23327,7412,4928.229,72127,3182,4038.129,89927,4262,4738.329,41527,2222,1937.528,56026,8201,7406.128,65327,2151,4385.028,33527,0181,3174.628,12826,8231,3054.627,93826,7741,1644.227,83126,6981,1334.155,91382.7
58192236117NYWayne County, NY1.01.01.048,72246,8581,8643.848,69446,4522,2424.649,09646,0353,0616.248,70145,8232,8785.948,51545,8432,6725.548,98746,5882,3994.948,60346,3482,2554.647,56445,3172,2474.748,18945,3692,8205.947,69143,7253,9668.347,42943,2274,2028.946,44542,5333,9128.446,52942,5363,9938.645,96442,4823,4827.644,40041,6552,7456.244,28741,9312,3565.043,98041,7892,1915.043,65741,4952,1625.043,77441,9581,8164.143,83542,0701,7654.061,11890.3
59192336119NYWestchester County, NY1.01.01.0461,112445,40115,7113.4463,716446,21317,5033.8471,246450,01221,2344.5472,045450,95021,0954.5477,966456,74121,2254.4482,484463,18819,2964.0487,426469,03418,3923.8489,581471,44018,1413.7495,831472,08823,7434.8487,641453,09634,5457.1478,676443,47635,2007.4473,504440,02133,4837.1477,481442,82734,6547.3477,670447,79529,8756.3470,532446,37824,1545.1478,437456,72821,7095.0477,692457,22120,4714.3480,739459,13721,6024.5482,058463,31518,7433.9484,382466,15818,2243.894,521139.7
60192436121NYWyoming County, NY6.04.00.020,23619,2749624.820,30519,3109954.921,20620,0001,2065.721,09119,8211,2706.020,99919,6911,3086.221,43120,3071,1245.221,82420,7671,0574.821,42420,3751,0494.921,39220,0621,3306.221,03019,1471,8839.019,45717,6161,8419.518,88117,2321,6498.718,90217,2291,6738.918,81817,3271,4917.918,35017,1531,1976.518,37717,3421,0356.018,10217,1379655.317,90716,9031,0045.617,96917,1358344.618,13117,3367954.457,70985.3
61192536123NYYates County, NY1.01.01.012,05511,6054503.712,02511,5674583.812,58411,9756094.812,88612,2706164.813,05712,4645934.512,99512,4365594.313,05612,5095474.213,05512,5085474.213,24212,5946484.913,23312,3418926.712,23011,3279037.412,08611,1819057.512,09011,1479437.811,97711,1588196.811,69011,0286625.711,74211,1575855.011,71311,1965174.411,62211,1145084.411,78411,3394453.811,84911,4074423.751,08975.5